Pith. sign in

REVIEW 5 major objections 9 minor 7 cited by

Imagine360: Immersive 360 Video Generation from Perspective Anchor

T0 review · 5 major / 9 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Imagine360 claims to be the first framework to lift an ordinary perspective video into a full 360-degree equirectangular video, and reports that it beats existing text- and image-guided 360° video generators on visual quality and motion…

desk verdict Solid dual-branch 360 video generator, but the 'first perspective-to-360' claim is contradicted by VidPanos, which the paper cites but never compares against. read the letter →

arxiv 2412.03552 v1 pith:WFEQFW6H submitted 2024-12-04 cs.CV

Jing Tan , Shuai Yang , Tong Wu , Jingwen He , Yuwei Guo , Ziwei Liu , Dahua Lin This is my paper · ORCID
classification cs.CV
keywords 360-degreevideogenerationperspective-to-360outpaintingdiffusionmodelsantipodalattentionelevation-awaremaskingequirectangularprojectionpanorama
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Imagine360 sets out to establish that an ordinary narrow-field-of-view perspective video, the kind a phone records, can be turned into a complete 360-degree equirectangular video in which the unseen surroundings are visually consistent and move coherently with the input clip. The paper's central claim is that this is achievable with a dual-branch video diffusion model that denoises the panorama and perspective domains side by side, an antipodal attention mask that enforces the reversed motion seen between opposite hemispheres, and elevation-aware masking that adapts to changing camera pitch. If the claim holds, immersive spherical video creation no longer requires panoramic cameras, panoramic optical flow, or high-quality panorama images as inputs; everyday footage becomes a sufficient anchor. The paper reports that its model outperforms existing 360° video generation methods on frame quality, motion smoothness, subject consistency, and human-rated structure plausibility.

What carries the argument

The central mechanism is the dual-branch video denoising U-Net with cross-domain spherical attention. A panorama branch denoises the full equirectangular latent, while a perspective branch projects that same latent into 20 perspective views through icosahedron modeling and denoises them with a pre-trained perspective video prior; both branches are built from space-time disentangled video diffusion U-Nets, with spatial LoRA layers and a fine-tuned panorama motion module. At the end of each block, the two branches exchange information through attention whose mask highlights two kinds of correspondences: the spherical mask for pixels directly mapped between panorama and perspective domains, and the antipodal mask for each pixel's opposite-hemisphere counterpart, which extends the receptive field across the sphere and encodes the reversed-motion relationship. Circular padding keeps the left and right edges of the equirectangular frame continuous, and elevation-aware mask positional encodings condition the model on the estimated pitch trajectory.

What would settle it

Take a perspective video with a measured, sharply nonlinear pitch trajectory (for example, a drone that tilts down 30° and pauses), run the inference pipeline, and compare the projected anchor region against the actual frames; if the anchor drifts or geometry distorts wherever the estimated pitch deviates from the measured trajectory, then the elevation-aware inference fails on general inputs. A second decisive check is to feed the same 40-frame clip with yaw set to a nonzero constant; if the output no longer matches the input view or the antipodal motion reverses incorrectly, the zero-yaw assumption is violated for panning footage.

Watch

Extended reading notes

Core claim

The paper claims to be the first perspective-to-360° video generation framework, and the design goal is to make the generated sphere follow the anchor video's appearance and motion while plausibly inventing everything outside the anchor's field of view. The discovery is that the strong generative prior of perspective video diffusion survives the transfer to the spherical domain if the model denoises a global panorama latent and local perspective views in parallel and couples them through cross-domain attention at every block. The antipodal mask sharpens that coupling by explicitly linking each pixel with its counterpart on the opposite hemisphere, so the model learns that forward motion in the viewing direction implies backward motion in the opposite direction. The elevation-aware designs let the mask geometry follow the input's pitch trajectory, so the anchor can sit higher or lower in the sphere across frames. The paper's experiments, including human evaluation, report the best graphics quality, motion smoothness, subject consistency, and panorama structure plausibility among the compared methods.

Load-bearing premise

The load-bearing premise is that the off-the-shelf single-image pitch estimator, after linear regression smoothing, yields a correct enough elevation trajectory to place the anchor video on the spherical canvas; if the elevation estimate is off, the generated 360° video contains artifacts, as the paper itself notes.

Editorial extensions

If this is right

  • Perspective video anchors can replace text prompts or panoramic optical flow as the control signal for 360° video generation, opening the task to ordinary recorded footage.
  • Fine-tuning only spatial LoRA layers plus the panorama-branch motion module, while freezing the perspective motion module, is sufficient to learn spherical motion from a few thousand panorama videos.
  • Explicit antipodal attention yields visible reversed motion: when the camera advances in the anchor view, objects in the opposite view recede.
  • Elevation-aware masking and linear smoothing of the pitch estimate reduce geometry artifacts in videos with changing camera tilt.
  • The same trained pipeline performs panorama image outpainting, producing more style-coherent results than dedicated image-based panorama outpainting methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the antipodal mask is a generic architectural prior for spherical coordinates, it could be inserted into other equirectangular generative models, such as text-to-360° video generators, to improve long-range motion consistency without retraining from scratch.
  • A testable extension is to feed the same anchor video at different yaw offsets; if the 'viewer rotates with the camera' assumption holds, the generated panorama should be equivalent up to a horizontal shift, and the antipodal motion should reverse accordingly.
  • The method's reliance on a single-image pitch estimator implies that a video-native or temporally aware elevation estimator would likely reduce artifacts on handheld footage with rapid, nonlinear tilts—an improvement the paper itself flags as future work.
  • The antipodal-motion prior could transfer to other spherical vision tasks, such as 360° video inpainting or depth completion, wherever opposite-hemisphere consistency is physically required.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 9 minor

Summary. The paper introduces Imagine360, a perspective-to-360° video generation framework that takes a narrow-FOV perspective video as anchor and generates a full equirectangular 360° video. The method builds on a dual-branch diffusion design (a panorama branch and a perspective branch) with cross-domain spherical attention augmented by an antipodal mask, plus elevation-aware data sampling and inference-time elevation estimation. The authors fine-tune spatial LoRA layers and motion modules on a combination of WEB360 and a newly collected dataset of 8,630 YouTube 360° videos. Quantitative comparisons on 100 test cases use Vbench metrics and Q-Align VQA, plus a human study, and ablations cover the dual-branch design, antipodal mask, elevation-aware designs, and fine-tuning strategy.

Significance. If the claims hold, Imagine360 would be a useful step toward user-friendly 360° video creation, because it uses only a perspective video as input rather than requiring text prompts plus optical flow or high-quality panoramic images. The dual-branch structure with antipodal attention is a reasonable way to inject global spherical constraints into a pretrained video diffusion model, and the elevation-aware mask handling addresses a real practical issue. The paper also contributes a nontrivial dataset and reports ablations for the main design choices. However, the central novelty claim and the superiority claim are weakened by the omission of VidPanos, a directly relevant prior method, and by evaluation concerns: the test set is undescribed, the baselines receive different input modalities, and no error bars or significance tests are given. The architecture itself appears defensible, but the load-bearing claims need substantial revision and supporting evidence.

major comments (5)
  1. [Sec. 2.3 and Sec. 5] The abstract, Sec. 1, and Sec. 5 repeatedly claim 'the first perspective-to-360° video generation framework.' Yet Sec. 2.3 cites VidPanos [31], which takes casually captured panning perspective videos and produces panoramic videos that can be explored interactively. That is the same input condition and output modality as the task defined in Sec. 1. The paper neither describes VidPanos nor compares against it, and the blanket statement that such methods 'struggle to bridge the distribution gap between panoramic and perspective videos' is made without evidence specific to VidPanos. Because the novelty claim is a binary assertion and the performance claim needs a head-to-head comparison against the most relevant prior method, this is a load-bearing omission.
  2. [Sec. 4.1 and Table 1] The comparison in Table 1 does not establish 'superior graphics quality and motion coherence among state-of-the-art 360° video generation methods' because the baselines are given different input modalities: 360DVD receives text and optical flow, AnimateDiff+LatentLab360 receives only the first frame, and Follow-Your-Canvas receives a masked perspective canvas. Only Imagine360 receives the full perspective video. The relative gains could partly reflect the additional conditioning information rather than the proposed architecture. The paper should either compare against methods that accept the same input condition (e.g., VidPanos) or restrict the claim to methods with similar input settings.
  3. [Sec. 4.2] The quantitative evaluation is performed on 'a total of 100 test cases' with no description of where these cases come from, how they are selected, or whether they overlap with the 8,630 YouTube videos collected in Sec. 3.5. If the test videos are drawn from the same distribution as the training data, the reported numbers would be inflated. Additionally, no error bars, confidence intervals, or significance tests are reported; several differences are small (e.g., Motion Smoothness 0.9806 vs. 0.9771, Subject Consistency 0.9710 vs. 0.9629). The paper should describe the test set construction, verify no overlap with training data, and report variance across multiple runs or statistical significance.
  4. [Sec. 3.4] The claim that 'without loss of generality, we can set the azimuthal angle to zero by assuming the viewer is rotating with the camera' is not a valid simplification for general perspective video inputs. It fails for input clips that pan, which are common in casually captured videos. The method as described can adjust for pitch changes but cannot adapt to changing yaw. Since the introduction and Sec. 3.4 describe the goal as handling 'diverse perspective video inputs' and 'general video inputs,' the paper should state clearly that panning videos are out of scope, or extend the method to handle yaw variation.
  5. [Sec. 3.4 and Limitations] The pipeline's robustness depends on an off-the-shelf single-image elevation estimator (PerspectiveFields) followed by LinearRegression smoothing. The authors acknowledge in the Limitations paragraph that inaccurate elevation estimation can produce artifacts, and Table D shows that smoothing is important. However, no analysis is given of how estimation errors translate into mask errors or how large the resulting artifacts can be across the 100 test cases. Since the elevation-aware design is one of the three key contributions, the paper should quantify the estimator's accuracy on the test set or provide a sensitivity analysis around the estimated pitch angles.
minor comments (9)
  1. [Sec. 4.1] The sentence 'Since we are the first perspective-to-360° video generation framework, it's infeasible to find a method that has the exactly same input condition as ours' repeats the novelty claim that is contradicted by VidPanos [31] and should be revised.
  2. [Sec. 3.1] The notation for the elevation angle is inconsistent: the text uses both `phi` (pitch) and `theta` (yaw) in the camera pose `(FOV, theta_{1:T}, phi_{1:T})`, but later the mask positional encoding concatenates a sinusoidal embedding for `theta`; clarify which angle is being encoded.
  3. [Sec. 2.2 and Sec. 2.3] The references [54] and [55] appear to be the same paper (Taming Stable Diffusion for Text to 360 Panorama Image Generation); this duplication should be fixed.
  4. [Fig. 2] Figure 2 is dense and the text labels 'Spatial Layer Spatial LoRA Motion Module' are difficult to parse; a clearer legend or separate subfigures would improve readability.
  5. [Sec. 3.4] The phrase 'we can set the azimuthal angle to zero by assuming the viewer is rotating with the camera' conflates a coordinate-system choice with an assumption about the input video; the paper should separate these.
  6. [Sec. 4.2 and Table 3] The human evaluation in Table 3 reports average rankings but does not state how many videos were judged per method or whether the evaluators were blinded to method identity; this information should be added.
  7. [Sec. B.2 and Table C] When retraining 360DVD on the authors' data, the paper does not state how many training steps or which hyperparameters were used; a fair comparison of the data contribution requires this detail.
  8. [Sec. A and Table A] The comparison to SIG-SS [18] should specify that this method was designed for a different task (spherical image generation from a few normal-FOV images) and that the comparison is therefore indirect.
  9. [Sec. 4.1] The typo 'GPUss' should be corrected to 'GPUs'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are supported by external benchmarks and ablations, while the VidPanos novelty gap is a completeness concern rather than a circular derivation.

full rationale

Imagine360's derivation chain is self-contained. The model is trained with standard denoising objectives (Eqs. 1-2) on paired target panorama and perspective projections, and the reported graphics-quality and motion-coherence claims are checked against external metrics (Vbench, Q-Align) and a human study, not against the training loss or against any parameter fitted to the evaluation set. The architecture borrows the dual-branch design, circular padding, IP-adapter conditioning, and off-the-shelf elevation estimation from cited prior work; these borrowings do not encode the target result, and the self-citations (AnimateDiff, LayerPano3D, Venhancer) are used only as backbone or utility components whose validity is independently established by their own evaluations. The stated limitation that inaccurate PerspectiveFields elevation estimates can cause artifacts is a genuine input dependency, not a disguised fit. The most notable concern is not circularity: the claim of being 'the first perspective-to-360° video generation framework' is weakened by the paper's own citation of VidPanos [31], a panorama-video method with a similar input condition, and Sec. 4.1's statement 'Since we are the first perspective-to-360 video generation framework, it's infeasible to find a method that has the exactly same input condition as ours' is a self-referential justification for omitting a close baseline. That is a novelty and evaluation-completeness weakness, and the unspecified test-set source raises a possible data-overlap concern, but neither reduces any derived result to its inputs by construction, so the circularity score remains 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on empirical priors and hand-set hyperparameters, not on a closed-form derivation. The domain assumptions (transferable pretrained priors, geometric projection, representative data) are plausible and partially ablated, but the most fragile item is the ad hoc yaw simplification and the off-the-shelf elevation estimator.

free parameters (5)
  • number of perspective views m = 20
    The perspective branch projects the spherical latent into m=20 icosahedron views (Sec. 3.2); this design choice controls local constraints and compute.
  • spatial LoRA rank and alpha = rank=32, alpha=1.0
    LoRA configuration for both branches (Sec. 4.1); chosen by hand for limited-data fine-tuning.
  • elevation sampling range = phi_s in (-20, 20) degrees
    Random starting pitch for training trajectories (Sec. 3.4); manually chosen to cover typical handheld tilt.
  • pitch slope ratio k = k in (-0.5, 0.5)
    Random slope for pitch trajectory augmentation (Sec. 3.4).
  • static clip filter threshold = 10% frames with mean flow > 0.1
    Heuristic to remove near-static videos from the web corpus (Sec. C.1).
assumptions (4)
  • domain assumption Pre-trained perspective diffusion priors (Stable Diffusion v2.1 and Follow-Your-Canvas motion module) can be fine-tuned with limited panorama data to model spherical video distributions.
    The dual-branch design and LoRA/full-motion fine-tuning depend on this transfer; only empirical ablations support it (Sec. 3.2, B.1).
  • standard math P2E/E2P projection with known FOV, yaw, and pitch produces geometrically correct masks and pixel correspondences between perspective and equirectangular domains.
    The mask construction and cross-domain spherical attention assume exact projection mapping (Sec. 3.1, 3.2).
  • domain assumption The collected 8,630 YouTube 360-video clips are a representative sample of the target distribution with diverse structured motion.
    Training data is self-collected and filtered; its coverage of urban and dynamic scenes is asserted, not measured (Sec. 3.5).
  • ad hoc to paper Yaw can be set to zero without loss of generality because the viewer rotates with the camera.
    This simplification ignores camera panning in the input video, which is common in real footage; it is stated in Sec. 3.4.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Imagine360: Immersive 360 Video Generation from Perspective Anchor." pith.science (2026). https://pith.science/paper/WFEQFW6H

@misc{pith2026241203552,
  author       = {Pith},
  title        = {Pith review of: Imagine360: Immersive 360 Video Generation from Perspective Anchor},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WFEQFW6H}},
  note         = {Machine review of arXiv:2412.03552}
}
abstract

$360^\circ$ videos offer a hyper-immersive experience that allows the viewers to explore a dynamic scene from full 360 degrees. To achieve more user-friendly and personalized content creation in $360^\circ$ video format, we seek to lift standard perspective videos into $360^\circ$ equirectangular videos. To this end, we introduce Imagine360, the first perspective-to-$360^\circ$ video generation framework that creates high-quality $360^\circ$ videos with rich and diverse motion patterns from video anchors. Imagine360 learns fine-grained spherical visual and motion patterns from limited $360^\circ$ video data with several key designs. 1) Firstly we adopt the dual-branch design, including a perspective and a panorama video denoising branch to provide local and global constraints for $360^\circ$ video generation, with motion module and spatial LoRA layers fine-tuned on extended web $360^\circ$ videos. 2) Additionally, an antipodal mask is devised to capture long-range motion dependencies, enhancing the reversed camera motion between antipodal pixels across hemispheres. 3) To handle diverse perspective video inputs, we propose elevation-aware designs that adapt to varying video masking due to changing elevations across frames. Extensive experiments show Imagine360 achieves superior graphics quality and motion coherence among state-of-the-art $360^\circ$ video generation methods. We believe Imagine360 holds promise for advancing personalized, immersive $360^\circ$ video creation.

Figures

Figures reproduced from arXiv: 2412.03552 by the authors.

Figure 1
Figure 1. Overview of Imagine360. Imagine360 lifts standard perspective video into 360◦ video, enabling dynamic scene experience from full 360 degrees. Compared to Follow-Your-Canvas, which focuses only on perspective visual and motion patterns, our approach achieves more plausible spherical video patterns. Best viewed with Acrobat Reader for the animated 360 videos. Abstract 360◦ videos offer a hyper-immersive experience tha… view at source ↗
Figure 2
Figure 2. Pipeline of Imagine360. Given perspective anchor video guidance, Imagine360 leverages a dual-branch video noising structure, with parallelled panorama and perspective branches to denoise 360° videos with plausible panoramic patterns. Additionally, we devise the cross-domain spherical attention with antipodal masking to capture long-range dependencies for reversed antipodal motion. Finally, we introduce elevation-awa… view at source ↗
Figure 3
Figure 3. Cross-domain Spherical Attention (perspective branch) highlights interaction for direct-mapped pixels (spherical mask) and antipodal-mapped pixels (antipodal mask) between panorama and perspective domains. the limited data and GPU resources. The training objectives of the trainable modules in the two branches are: LE = EE(x1:T E ),y,ϵ1:T E ,t h ϵE − ϵE,Θ [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Elevation-aware sampling augments the training sam￾ples with diverse elevation trajectories. viewer. In both the panoramic branch and the perspective branch, the antipodal pixels are usually distant from each other in the token sequence, whereas the attention modules o…
Figure 5
Figure 5. Figure 5: Qualitative comparisons on 360° video generations among state-of-the-art methods. Imagine360 generates 360° video genera￾tion with superior visual quality and plausible panoramic patterns. Method Imaging Quality ↑ Aesthetic Quality ↑ Motion Smoothness ↑ Subject Consist…
Figure 6
Figure 6. Figure 6: Qualitative ablation on the dual-branch design shows that dual-branch design generates plausible panoramic patterns compared to single-branch. we present the panorama frame with four projected per￾spective views (ϕ = 0◦ , θ = [0◦ , 90◦ , 180◦ , 270◦ ]) to make it easie…
Figure 8
Figure 8. Figure 8: Qualitative ablation on the elevation-aware designs shows reduced artifacts from the elevation-aware designs . Ablation on antipodal mask [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A panorama representation and attention scheme that lets a pretrained perspective video diffusion model generate spatially consistent 360-degree videos from an input perspective clip.

  2. Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Leader360V provides a 10,000+ video, 198-class, densely annotated 360-degree video dataset with an LLM-assisted automatic annotation pipeline, and shows fine-tuning on it improves 360 video segmentation and tracking models.

  3. PanoWan: Lifting Diffusion Video Generation Models to 360{\deg} with Latitude/Longitude-aware Mechanisms

    cs.CV 2025-05 conditional novelty 6.0 of 10

    PanoWan adapts the Wan 2.1 text-to-video model to generate seamless 360-degree videos by remapping initial noise, rotating the latent grid during denoising, and padding the latent before VAE decoding, trained on a new...

  4. HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene Generation

    cs.CV 2025-04 conditional novelty 6.0 of 10

    A framework that generates panoramic videos from one image and reconstructs them into 4D Gaussian scenes, with a new panoramic video dataset and improved depth alignment.

  5. Gimbal360: Canonicalizing Planar Diffusion for Spherical Panorama Completion

    cs.CV 2026-03 reject novelty 5.0 of 10

    Gimbal360 completes 360° panoramas from unposed perspective images by rigidly auto-leveling inputs and training diffusion with a Siamese shift-equivariance loss to preserve ERP seam continuity.

  6. PanoLora: Bridging Perspective and Panoramic Video Generation with LoRA Adaptation

    cs.CV 2025-09 reject novelty 5.0 of 10

    Fine-tuning a pretrained video diffusion model with LoRA rank 16 on about 1,000 synthetic videos produces panoramic video with good seam closure, but the claim that rank must exceed 8 degrees of freedom is not proven.

  7. TiP4GEN: Text to Immersive Panorama 4D Scene Generation

    cs.CV 2025-08 conditional novelty 5.0 of 10

    TiP4GEN generates motion-rich, geometry-consistent 360-degree 4D scenes from a global text prompt plus four local perspective prompts, using a dual-branch video diffusion model with bidirectional cross-attention and a...

Reference graph

Works this paper leans on

57 extracted references · 29 canonical work pages · cited by 7 Pith papers

  1. [31]

    Vidpanos: Generative panoramic videos from casual panning videos

    Jingwei Ma, Erika Lu, Roni Paiss, Shiran Zada, Aleksander Holynski, Tali Dekel, Brian Curless, Michael Rubinstein, and Forrester Cole. Vidpanos: Generative panoramic videos from casual panning videos. In SIGGRAPH Asia 2024 Con- ference Papers, 2024. 3

    work page 2024

  2. [1]

    Di- verse plausible 360-degree image outpainting for efficient 3dcg background creation

    Naofumi Akimoto, Yuhi Matsuo, and Yoshimitsu Aoki. Di- verse plausible 360-degree image outpainting for efficient 3dcg background creation. CVPR, 2022. 3

    work page 2022

  3. [2]

    Stable video diffusion: Scaling latent video diffusion models to large datasets

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. 3

    arXiv 2023

  4. [3]

    Align your latents: High-resolution video synthesis with la- tent diffusion models

    Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dock- horn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with la- tent diffusion models. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 22563–22575, 2023. 3

    2023

  5. [4]

    Videocrafter1: Open diffusion models for high-quality video generation

    Haoxin Chen, Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, et al. Videocrafter1: Open diffusion models for high-quality video generation. arXiv preprint arXiv:2310.19512, 2023

    arXiv

  6. [5]

    Videocrafter2: Overcoming data limitations for high-quality video diffusion models

    Haoxin Chen, Yong Zhang, Xiaodong Cun, Menghan Xia, Xintao Wang, Chao Weng, and Ying Shan. Videocrafter2: Overcoming data limitations for high-quality video diffusion models. arXiv preprint arXiv:2401.09047, 2024. 3

    arXiv 2024

  7. [6]

    Follow-your-canvas: Higher-resolution video outpainting with extensive content generation.arXiv preprint arXiv:2409.01055, 2024

    Qihua Chen, Yue Ma, Hongfa Wang, Junkun Yuan, Wenzhe Zhao, Qi Tian, Hongmei Wang, Shaobo Min, Qifeng Chen, and Wei Liu. Follow-your-canvas: Higher-resolution video outpainting with extensive content generation.arXiv preprint arXiv:2409.01055, 2024. 3, 4, 6, 7, 8

    arXiv 2024

  8. [7]

    Text2light: Zero-shot text-driven hdr panorama generation

    Zhaoxi Chen, Guangcong Wang, and Ziwei Liu. Text2light: Zero-shot text-driven hdr panorama generation. ACM Trans- actions on Graphics (TOG), 41(6):1–16, 2022. 3

    2022

Show all 57 references
  1. [8]

    Inout: Diverse im- age outpainting via GAN inversion

    Yen-Chi Cheng, Chieh Hubert Lin, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, and Ming-Hsuan Yang. Inout: Diverse im- age outpainting via GAN inversion. In CVPR, pages 11421– 11430. IEEE, 2022. 3

    2022

  2. [9]

    Videollama 2: Advancing spatial- temporal modeling and audio understanding in video-llms

    Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, et al. Videollama 2: Advancing spatial- temporal modeling and audio understanding in video-llms. arXiv preprint arXiv:2406.07476, 2024. 6

    2024 arXiv

  3. [10]

    Scaling recti- fied flow transformers for high-resolution image synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M ¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling recti- fied flow transformers for high-resolution image synthesis. In Forty-first International Conference on Mach...

    2024

  4. [11]

    Hierar- chical masked 3d diffusion model for video outpainting

    Fanda Fan, Chaoxu Guo, Litong Gong, Biao Wang, Tiezheng Ge, Yuning Jiang, Chunjie Luo, and Jianfeng Zhan. Hierar- chical masked 3d diffusion model for video outpainting. In Proceedings of the 31st ACM International Conference on Multimedia, pages 7890–7900, 2023. 3

    2023

  5. [12]

    Diffusion360: Seamless 360 degree panoramic image generation based on diffusion models, 2023

    Mengyang Feng, Jinlin Liu, Miaomiao Cui, and Xuansong Xie. Diffusion360: Seamless 360 degree panoramic image generation based on diffusion models, 2023. 3, 11

    2023

  6. [13]

    Lumina-t2x: Transforming text into any modality, reso- lution, and duration via flow-based large diffusion transform- ers

    Peng Gao, Le Zhuo, Ziyi Lin, Chris Liu, Junsong Chen, Ruoyi Du, Enze Xie, Xu Luo, Longtian Qiu, Yuhang Zhang, et al. Lumina-t2x: Transforming text into any modality, reso- lution, and duration via flow-based large diffusion transform- ers. arXiv preprint arXiv:2405.05945, 2024. 3

    2024 arXiv

  7. [14]

    Im- age style transfer using convolutional neural networks

    Leon A Gatys, Alexander S Ecker, and Matthias Bethge. Im- age style transfer using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2414–2423, 2016. 11

    2016

  8. [15]

    I2v-adapter: A general image- to-video adapter for video diffusion models

    Xun Guo, Mingwu Zheng, Liang Hou, Yuan Gao, Yufan Deng, Chongyang Ma, Weiming Hu, Zhengjun Zha, Haibin Huang, Pengfei Wan, et al. I2v-adapter: A general image- to-video adapter for video diffusion models. arXiv preprint arXiv:2312.16693, 2023. 3

    2023 arXiv

  9. [16]

    Animatediff: Animate your personalized text- to-image diffusion models without specific tuning

    Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text- to-image diffusion models without specific tuning. arXiv preprint arXiv:2307.04725, 2023. 3, 4, 5, 7, 8

    2023 arXiv

  10. [17]

    Sparsectrl: Adding sparse controls to text-to-video diffusion models

    Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Sparsectrl: Adding sparse controls to text-to-video diffusion models. In European Conference on Computer Vision, pages 330–348. Springer, 2025. 3

    2025

  11. [18]

    Spher- ical image generation from a few normal-field-of-view im- ages by considering scene symmetry

    Takayuki Hara, Yusuke Mukuta, and Tatsuya Harada. Spher- ical image generation from a few normal-field-of-view im- ages by considering scene symmetry. IEEE Transactions on Pattern Analysis and Machine Intelligence , pages 1–15,

  12. [19]

    Venhancer: Generative space-time enhancement for video generation

    Jingwen He, Tianfan Xue, Dongyang Liu, Xinqi Lin, Peng Gao, Dahua Lin, Yu Qiao, Wanli Ouyang, and Ziwei Liu. Venhancer: Generative space-time enhancement for video generation. arXiv preprint arXiv:2407.07667, 2024. 5, 13 9

    2024 arXiv

  13. [20]

    Clipscore: A reference-free evaluation met- ric for image captioning

    Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation met- ric for image captioning. In EMNLP (1), pages 7514–7528. Association for Computational Linguistics, 2021. 11

    2021

  14. [21]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 3

    2020

  15. [22]

    Imagen video: High definition video generation with diffusion mod- els

    Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion mod- els. arXiv preprint arXiv:2210.02303, 2022. 3

    arXiv

  16. [23]

    Video dif- fusion models

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video dif- fusion models. Advances in Neural Information Processing Systems, 35:8633–8646, 2022. 3

    2022

  17. [24]

    Vbench: Com- prehensive benchmark suite for video generative models

    Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, Limin Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. Vbench: Com- prehensive benchmark suite for video generative models....

    2024

  18. [25]

    Perspective fields for single image cam- era calibration

    Linyi Jin, Jianming Zhang, Yannick Hold-Geoffroy, Oliver Wang, Kevin Blackburn-Matzen, Matthew Sticha, and David F Fouhey. Perspective fields for single image cam- era calibration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1730...

    2023

  19. [26]

    Segment any- thing

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C Berg, Wan-Yen Lo, et al. Segment any- thing. In Proceedings of the IEEE/CVF International Con- ference on Computer Vision, pages 4015–4026, 2023. 4

    2023

  20. [27]

    Free- man

    Dilip Krishnan, Piotr Teterwak, Aaron Sarna, Aaron Maschinot, Ce Liu, David Belanger, and William T. Free- man. Boundless: Generative adversarial networks for image extension. In ICCV, pages 10520–10529. IEEE, 2019. 3

    2019

  21. [28]

    Syncdiffusion: Coherent montage via synchronized joint diffusions, 2023

    Yuseung Lee, Kunho Kim, Hyunjin Kim, and Minhyuk Sung. Syncdiffusion: Coherent montage via synchronized joint diffusions, 2023. 3, 11

    2023

  22. [29]

    Panogen: Text-conditioned panoramic environment generation for vision-and-language navigation

    Jialu Li and Mohit Bansal. Panogen: Text-conditioned panoramic environment generation for vision-and-language navigation. In NeurIPS, 2023. 3

    2023

  23. [30]

    4k4dgen: Panoramic 4d generation at 4k resolution

    Renjie Li, Panwang Pan, Bangbang Yang, Dejia Xu, Shijie Zhou, Xuanyang Zhang, Zeming Li, Achuta Kadambi, Zhangyang Wang, and Zhiwen Fan. 4k4dgen: Panoramic 4d generation at 4k resolution. arXiv preprint arXiv:2406.13527, 2024. 2, 3

    2024 arXiv

  24. [32]

    BIPS: bi-modal in- door panorama synthesis via residual depth-aided adversarial learning

    Changgyoon Oh, Wonjune Cho, Yujeong Chae, Daehee Park, Lin Wang, and Kuk-Jin Yoon. BIPS: bi-modal in- door panorama synthesis via residual depth-aided adversarial learning. In ECCV (16), pages 352–371. Springer, 2022. 3

    2022

  25. [33]

    SDXL: improving latent diffusion models for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. SDXL: improving latent diffusion models for high-resolution image synthesis. CoRR, 2023. 3

    2023

  26. [34]

    360monodepth: High-resolution 360deg monocular depth estimation

    Manuel Rey-Area, Mingze Yuan, and Christian Richardt. 360monodepth: High-resolution 360deg monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 3762– 3772, 2022. 5

    2022

  27. [35]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 3

    2022

  28. [36]

    Panoflow: Learning 360° optical flow for surrounding tem- poral understanding

    Hao Shi, Yifan Zhou, Kailun Yang, Xiaoting Yin, Ze Wang, Yaozu Ye, Zhe Yin, Shi Meng, Peng Li, and Kaiwei Wang. Panoflow: Learning 360° optical flow for surrounding tem- poral understanding. IEEE Transactions on Intelligent Transportation Systems, 24(5):5570–5585, 2023. 6

    2023

  29. [37]

    Generative modeling by es- timating gradients of the data distribution

    Yang Song and Stefano Ermon. Generative modeling by es- timating gradients of the data distribution. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2019. 3

    2019

  30. [38]

    Score-based generative modeling through stochastic differential equa- tions

    Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Ab- hishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equa- tions. arXiv preprint arXiv:2011.13456, 2020. 3

    2011 arXiv

  31. [39]

    Transnet v2: An effective deep network architecture for fast shot transition detection

    Tom ´aˇs Souˇcek and Jakub Loko ˇc. Transnet v2: An effective deep network architecture for fast shot transition detection. arXiv preprint arXiv:2008.04838, 2020. 6

    2008 arXiv

  32. [40]

    Ldm3d: Latent diffusion model for 3d, 2023

    Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. Ldm3d: Latent diffusion model for 3d, 2023. 3

    2023

  33. [41]

    Be-your-outpainter: Mastering video outpainting through input-specific adaptation

    Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang, Xiaoyu Shi, Dazhong Shen, Guanglu Song, Yu Liu, and Hongsheng Li. Be-your-outpainter: Mastering video outpainting through input-specific adaptation. In European Conference on Com- puter Vision, pages 153–168. Springer, 2025. 3

    2025

  34. [42]

    Stylelight: Hdr panorama generation for lighting estimation and editing, 2022

    Guangcong Wang, Yinuo Yang, Chen Change Loy, and Zi- wei Liu. Stylelight: Hdr panorama generation for lighting estimation and editing, 2022. 3

    2022

  35. [43]

    Customizing 360-degree panoramas through text-to-image diffusion models

    Hai Wang, Xiaoyu Xiang, Yuchen Fan, and Jing-Hao Xue. Customizing 360-degree panoramas through text-to-image diffusion models. In WACV, pages 4921–4931. IEEE, 2024. 3

    2024

  36. [44]

    360-degree panorama generation from few unregis- tered nfov images

    Jionghao Wang, Ziyu Chen, Jun Ling, Rong Xie, and Li Song. 360-degree panorama generation from few unregis- tered nfov images. In ACM Multimedia, pages 6811–6821. ACM, 2023. 3

    2023

  37. [45]

    Modelscope text-to-video technical report

    Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, and Shiwei Zhang. Modelscope text-to-video technical report. arXiv preprint arXiv:2308.06571, 2023. 3

    2023 arXiv

  38. [46]

    360dvd: Controllable panorama video generation with 360-degree video diffusion model

    Qian Wang, Weiqi Li, Chong Mou, Xinhua Cheng, and Jian Zhang. 360dvd: Controllable panorama video generation with 360-degree video diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6913–6923, 2024. 2, 3, 6, 7, 8, 12, 13 10

    2024

  39. [47]

    Q-align: Teaching lmms for visual scoring via discrete text-defined levels

    Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, Qiong Yan, Xiongkuo Min, Guang- tao Zhai, and Weisi Lin. Q-align: Teaching lmms for visual scoring via discrete text-defined levels. In ICML. OpenRe- ...

    2024

  40. [48]

    Cross-view panorama image synthesis

    Songsong Wu, Hao Tang, Xiao-Yuan Jing, Haifeng Zhao, Jianjun Qian, Nicu Sebe, and Yan Yan. Cross-view panorama image synthesis. IEEE Trans. Multim., 25:3546–3559, 2023. 3

    2023

  41. [49]

    Pan- odiffusion: 360-degree panorama outpainting via diffusion,

    Tianhao Wu, Chuanxia Zheng, and Tat-Jen Cham. Pan- odiffusion: 360-degree panorama outpainting via diffusion,

  42. [50]

    Dynamicrafter: Animating open-domain images with video diffusion priors

    Jinbo Xing, Menghan Xia, Yong Zhang, Haoxin Chen, Xin- tao Wang, Tien-Tsin Wong, and Ying Shan. Dynamicrafter: Animating open-domain images with video diffusion priors. arXiv preprint arXiv:2310.12190, 2023. 3

    2023 arXiv

  43. [51]

    Layer- pano3d: Layered 3d panorama for hyper-immersive scene generation

    Shuai Yang, Jing Tan, Mengchen Zhang, Tong Wu, Yixuan Li, Gordon Wetzstein, Ziwei Liu, and Dahua Lin. Layer- pano3d: Layered 3d panorama for hyper-immersive scene generation. arXiv preprint arXiv:2408.13252, 2024. 5

    2024 arXiv

  44. [52]

    Cogvideox: Text-to-video diffusion models with an expert transformer

    Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiao- han Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072, 2024. 3

    2024 arXiv

  45. [53]

    Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models

    Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models. arXiv preprint arXiv:2308.06721,

    arXiv

  46. [54]

    Taming stable diffusion for text to 360 panorama image generation

    Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xi- aoshui Huang, Dinh Phung, Wanli Ouyang, and Jianfei Cai. Taming stable diffusion for text to 360 panorama image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6347– 6...

    2024

  47. [55]

    Taming stable diffusion for text to 360 panorama image generation

    Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xi- aoshui Huang, Dinh Phung, Wanli Ouyang, and Jianfei Cai. Taming stable diffusion for text to 360 panorama image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6347– 6...

    2024

  48. [56]

    I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models

    Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, and Jingren Zhou. I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models. arXiv preprint arXiv:2311.04145, 2023. 3

    2023 arXiv

  49. [57]

    Pia: Your personalized image animator via plug-and-play modules in text-to-image models

    Yiming Zhang, Zhening Xing, Yanhong Zeng, Youqing Fang, and Kai Chen. Pia: Your personalized image animator via plug-and-play modules in text-to-image models. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7747–7756, 2024. 3 A. Di...

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.