Pith. sign in

REVIEW 5 major objections 5 minor 2 cited by

Can video generation replace cinematographers? Research on the cinematic language of generated video

T0 review · 5 major / 5 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 The paper claims that a text-to-video pipeline can independently control shot framing, shot angle, and camera movement, and can smoothly blend several such controls within a single generated video.

desk verdict A broad cinematic-language dataset and a sensible pipeline, but the headline numbers compare fine-tuned to zero-shot models and the generation claims lack content-controlled human evaluation. read the letter →

arxiv 2412.12223 v2 pith:N6C4LEBS submitted 2024-12-16 cs.CV cs.AI

Xiaozhe Li , Kai WU , Siyi Yang , YiZhan Qu , Guohua.Zhang , Zhiyu Chen , Jiayao Li , Jiangchuan Mu
show 7 more authors
Xiaobin Hu Wen Fang Mingliang Xiong Hao Deng Qingwen Liu Gang Li Bin He
This is my paper · ORCID
classification cs.CVcs.AI
keywords cinematiclanguagetext-to-videogenerationcameracontrolLoRAcompositionCLIPshotframinganglemovement
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

This paper tries to close the gap between text-to-video (T2V) generation and professional cinematography by giving T2V models explicit control over cinematic language. It introduces Cinematic2K, a dataset of about 2,000 real videos organized into twenty subcategories spanning shot framing, shot angles, and camera movements. On top of this dataset it builds CameraDiff, which trains one lightweight LoRA module per cinematic category on a frozen AnimateDiff backbone, and CLIPLoRA, which uses a fine-tuned CLIP model (CameraCLIP) as a fitness evaluator in a genetic search over LoRA activation sequences, enabling several cinematic attributes to be composed in one video. If the claim is right, a filmmaker could ask for a long shot, eye level, panning right, zoom in, tilt up view of a bear walking in snow near a lake and receive a single video that obeys all those directives, rather than just object motion.

What carries the argument

The load-bearing mechanism is the pairing of a frozen text-to-video diffusion backbone (AnimateDiff) with lightweight per-category LoRA adapters, one per cinematic language category, each trained on roughly 95–100 carefully verified video clips from Cinematic2K. The second half of the machinery is CameraCLIP, a fine-tuned CLIP model that scores how well a generated video matches a cinematic text description; CLIPLoRA turns that score into a fitness function for a genetic algorithm that searches over which LoRA should be active at each denoising step. The paper's core identity is therefore that a cinematic attribute corresponds to a LoRA module, and a cinematic sentence corresponds to a temporally scheduled composition of LoRA modules, selected by a CLIP-based evaluator.

What would settle it

Have professional cinematographers blind-rate CameraDiff outputs on prompts with scene content far outside the stock clips used for training, such as rack focus on a chess game in a library, and measure whether the requested shot attribute is reliably present; alternatively, measure CameraCLIP's R@1 on a held-out set where scene content is varied while the cinematic attribute is held fixed. If either the human ratings fall to chance or the retrieval score drops well below 0.83, the claimed stable, disentangled cinematic control collapses.

Watch

Extended reading notes

Core claim

The central claim is that stable, precise cinematic control in T2V generation reduces to per-category LoRA training plus CLIP-guided LoRA composition. CameraDiff yields twenty distinct cinematic patterns by fine-tuning a separate LoRA for each of the twenty categories on the frozen AnimateDiff backbone. CameraCLIP, built by fine-tuning only the last two text-encoder layers and the last four vision-encoder layers of CLIP and mean-pooling eight frames per video, reaches an R@1 score of 0.83 on the Cinematic2K validation set, above all compared video-CLIP models. CLIPLoRA then uses CameraCLIP as the fitness function in a genetic algorithm that searches for the optimal sequence of LoRA activations across denoising steps; the paper reports that this yields an FVD of 1837 and CLIPSIM of 0.2535, better than static LoRA merging, switching, or simple composition baselines.

Load-bearing premise

The argument assumes that the Cinematic2K annotations are accurate and that the twenty cinematic categories are separable enough that a LoRA trained on roughly 95–100 stock clips per category transfers to arbitrary prompts and produces visibly correct framing, angle, and movement in the frozen AnimateDiff backbone.

Editorial extensions

If this is right

  • Natural-language prompts can specify shot framing, shot angle, and camera movement simultaneously, without requiring camera parameter inputs such as extrinsic matrices or motion trajectories.
  • A single generated video can contain smooth transitions between multiple cinematic attributes, such as starting with a long shot, zooming in, and tilting up, which static LoRA merge methods produce with visual artifacts.
  • Cinematic text-video retrieval on this vocabulary reaches R@1 0.83, suggesting automated tagging and search of footage by shot attributes is feasible.
  • The CLIPLoRA genetic-search recipe gives a concrete way to adaptively compose any set of LoRAs during diffusion, not only cinematic ones.
  • The combination lifts FVD from above 2300 for the static composition baselines to 1837, pointing toward more temporally coherent composite shots.

Reading between the lines

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

  • The same per-category-LoRA plus CLIP-evaluator recipe could plausibly extend to lighting, lens length, color grade, or scene mood, since the paper's taxonomy is one choice of cinematic vocabulary rather than a necessary set.
  • Because CameraCLIP's ablation shows simple mean pooling beating Transformers and LSTM on this small dataset, the result suggests that for narrow, label-like attributes, complex temporal aggregators may overfit; a testable extension is whether this survives dataset growth.
  • The paper's title asks whether video generation can replace cinematographers; read strictly, the work demonstrates control over a finite vocabulary of shot attributes, not over aesthetic judgment, narrative pacing, or emotional intent, so replacement would require additional evidence the paper does not provide.
  • CLIPLoRA's genetic search uses generated videos on a set of test prompts as fitness, which makes the search cost proportional to the number of generations; an implicit extension is caching or distillation of the learned scheduling rule into a direct predictor to avoid per-prompt search.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper proposes a three-part system for cinematic control in text-to-video (T2V) generation. First, Cinematic2K is introduced as a dataset of roughly 2,000 stock videos annotated across twenty subcategories spanning shot framing, shot angle, and camera movement. Second, CameraCLIP is presented as a video-text alignment model obtained by fine-tuning the last layers of CLIP on Cinematic2K and mean-pooling eight frame features. Third, CameraDiff trains one LoRA per cinematic category on the frozen AnimateDiff backbone for single-shot control, and CLIPLoRA uses a genetic algorithm with CameraCLIP as fitness to select a sequence of LoRA activations across diffusion steps for multi-shot composition. Experiments report an R@1 of 0.83 for CameraCLIP on a Cinematic2K validation split, qualitative single- and multi-shot generation results, and CLIPSIM/FVD comparisons showing CLIPLoRA outperforming static LoRA composition baselines.

Significance. If the claims held, the work would fill a real gap: current T2V models offer limited control over framing, angle, and camera movement, and the proposed taxonomy plus LoRA-composition recipe would be a useful practical contribution. The dataset addresses an annotation gap relative to RealEstate10K, CineScale, and CineScale2, and the idea of using a video-text retriever to guide LoRA scheduling is interesting. However, as presented the evidence does not yet support the central claims. The headline retrieval comparison is not apples-to-apples, the generation evaluation is largely qualitative and uses metrics that do not verify camera attributes, and the CLIPLoRA search is optimized with the same evaluator used to score it. The paper is best viewed as a system proposal with promising components that need substantially stronger validation before the claims of stable, precise, and seamless cinematic control are justified.

major comments (5)
  1. [§4.2, Table 2] CameraCLIP is fine-tuned on the Cinematic2K training split, while all baselines (CLIP, CLIP4CLIP, ViCLIP, LongCLIP, VideoCLIP-XL) are evaluated zero-shot. The reported R@1 improvement from 0.77 to 0.83 may therefore reflect in-domain fine-tuning rather than superior cinematic understanding. The authors should fine-tune the baselines on the same training split or evaluate CameraCLIP on an external cinematic benchmark; otherwise the headline claim that CameraCLIP 'surpasses all baselines' is not established.
  2. [§3.3, Eq. (2); §4.4.2, Table 5] CLIPLoRA uses CameraCLIP as the genetic-algorithm fitness in Eq. (2) and then Table 5 compares CLIPLoRA against variants guided by other CLIP models. Since CameraCLIP was trained on the same Cinematic2K categories that the LoRAs are meant to express, the search is optimized toward the evaluator itself. The resulting CLIPSIM/FVD gains may reflect optimization to CameraCLIP rather than perceptible cinematic quality. The authors should report independent human judgments of framing, angle, and movement correctness, and should also test CLIPLoRA with a held-out evaluator or with an evaluator trained on disjoint categories to break the circularity.
  3. [§3.1, Steps I–III; §3.3] Each cinematic LoRA is trained on roughly 95–100 stock clips per category (Figure 3). Stock footage categories carry strong content priors: for example, 'bird angle' clips are often aerial landscape shots, and 'dolly in' clips often contain a walking subject. The paper does not provide any content-controlled experiment showing that a LoRA changes the camera attribute while leaving scene content fixed, and Figure 4 is qualitative only. The claim that CameraDiff provides 'stable and precise cinematic control' therefore lacks support. A controlled generation study with identical or matched prompts across categories, or a per-attribute human rating study, is needed.
  4. [§4.3, Table 3 and Figure 6] The quantitative generation metrics are not sufficient for the claim. FVD is computed against WebVid10M reference videos and CLIPSIM measures overall text-video similarity; neither metric verifies that the generated video actually exhibits the requested shot framing, angle, or camera movement. Moreover, no error bars, repeated runs, or significance tests are reported for the CLIPSIM/FVD differences, so the margins in Table 3 (e.g., 0.2535 vs. 0.2394) and Figure 6 may be within noise. The authors should add a human study or an attribute-level automatic metric, and report variance across seeds.
  5. [§4.4.1, Table 4] The temporal-modeling ablation is internally inconsistent: the text says mean pooling 'significantly outperformed' Transformer with an accuracy of 0.81, but Table 4 lists 0.83 for Mean Pooling and does not state which ViT backbone and input resolution are used for each row. Since Table 2 shows R@1 varies strongly with model size and resolution, the comparison must hold the backbone fixed and report the same configuration. This is needed to justify the mean-pooling design choice that underlies CameraCLIP.
minor comments (5)
  1. [§3.2, Eq. (1)] The notation for the video feature V as the mean of frame features I_i is clear, but the layer indices for the fine-tuned ViT layers (20–23) should be defined with respect to the specific CLIP variant (e.g., ViT-L/14) used in the main results.
  2. [§2] There is a typo in 'VAEs' and inconsistent capitalization of 'cameraCLIP' versus 'CameraCLIP' in the related-work section; the paper should use a single consistent name.
  3. [§4.4.1] The sentence 'consistent with prior work's conclusion [20]' is vague; the authors should state which conclusion from CLIP4CLIP they are referring to, or remove the citation.
  4. [§4.1] The FVD comparison would be more informative if the authors reported the reference-video distribution and the number of generated clips used, since FVD estimates are sensitive to sample size.
  5. [Figure 3] The donut-chart labels are difficult to read; listing exact per-category counts in the caption or in a table would improve clarity.

Circularity Check

1 steps flagged · score 5.0 of 10

Partial circularity: CLIPLoRA is optimized on CameraCLIP, and the Figure 6 curve is labeled 'CameraCLIP Metric'; independent CLIPSIM/FVD results in Table 3 prevent full collapse.

  1. fitted input called prediction [Section 3.3 Eq. (2); Section 4.3 Figure 6]
    "F (Li) = 1 |S| X s∈S CameraCLIP(Li, s). (2) ... First, we use CLIPSIM to measure cinematic text-video consistency as the number of LoRAs increases, as shown in Figure 6. ... Figure 6. Comparison of CLIPLoRA results with other LoRA composition methods. [y-axis:] CameraCLIP Metric"

    Equation (2) is the genetic-algorithm fitness: CLIPLoRA searches for LoRA activation sequences that maximize CameraCLIP(Li,s). Section 4.3 then presents a curve whose y-axis is labeled 'CameraCLIP Metric' as evidence that CLIPLoRA outperforms Origin, Merge, Switch, and Composite. Reporting superiority on the same score used as the optimization objective is partly guaranteed by construction, so this particular quantitative claim reduces to the fitness function. The circularity is only partial because Table 3 also reports CLIPSIM and FVD, which are not the optimized objective and independently favor CLIPLoRA. The R@1 retrieval result is a standard hold-out evaluation and is not circular.

full rationale

The paper contains no formal derivation chain that is circular by equation identity. CameraCLIP's R@1=0.83 is measured on a validation split of Cinematic2K after fine-tuning on a training split, which is normal hold-out benchmarking. CameraDiff's single-shot control claims are qualitative; the lack of a content-attribute separation experiment is a validity limitation rather than circularity. The one concrete circular step is the CLIPLoRA evaluation overlap: Eq. (2) defines the search objective as CameraCLIP, and Figure 6 labels the reported curve 'CameraCLIP Metric' despite the text referring to CLIPSIM. That makes the Figure 6 comparison partly tautological. The overall score is moderate rather than higher because Table 3's CLIPSIM and FVD values are independent of the optimized metric, so the central multi-shot composition claim retains independent empirical support.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central claims rest on labeled data and training choices rather than on a formal derivation. The key unverified inputs are the validity of the annotations, the separability of the 20 cinematic categories, and the transferability of stock-footage-trained LoRAs to arbitrary prompts. Free parameters include sampling, layer-selection, search, and LoRA training choices, several of which are unreported.

free parameters (6)
  • Number of sampled video frames N = 8
    Chosen as a coverage-compute tradeoff in Section 3.2; affects the video representation and all retrieval and generation scores.
  • Text encoder fine-tuning depth = last 2 layers (layers 10-11)
    Design choice in Section 3.2 to preserve CLIP generalization; no ablation is reported for this layer count.
  • ViT encoder fine-tuning depth = last 4 layers (layers 20-23)
    Design choice in Section 3.2 for visual adaptation; not swept in the experiments.
  • Genetic algorithm hyperparameters = not reported
    Population size, elite proportion p%, mutation probability pm, and generations T are defined in Section 3.3 but their values are deferred to the supplementary.
  • Fitness prompt set S = overlap with evaluation prompts not stated
    If S is the same set used for final CLIPSIM/FVD evaluation, the LoRA search is fitted to the evaluation distribution; Section 3.3 Eq. (2).
  • LoRA rank and training hyperparameters = not reported
    The capacity and optimization of each cinematic LoRA determine single-shot control quality; details are deferred to the supplementary.
assumptions (6)
  • domain assumption Cinematic language is decomposable into three primary types and twenty separable subcategories.
    Section 3.1 and Figure 3; the dataset structure and per-category LoRA training depend on this decomposition being learnable and composable.
  • domain assumption Automatic extraction plus human verification on stock footage from Pexels and Videvo yields reliable cinematic labels.
    Section 3.1 Steps I-III; the paper reports human annotation but gives no inter-annotator agreement or external validation of label quality.
  • ad hoc to paper Fine-tuning only the last CLIP layers preserves generalization while learning cinematic concepts.
    Section 3.2; the paper asserts this benefit but does not ablate the choice of fine-tuned layers.
  • domain assumption Mean pooling of eight frame features is sufficient temporal modeling for cinematic text-video alignment.
    Sections 3.2 and 4.4.1; supported only by an ablation on the small in-distribution validation set.
  • domain assumption FVD computed against WebVid10M references is a valid quality measure for cinematic videos.
    Section 4.1; the reference domain is general web video, not cinematic stock footage, so the metric may not capture cinematic quality.
  • ad hoc to paper CameraCLIP reward maximization in the genetic search improves independent quality metrics.
    Section 3.3 Eq. (2) and Table 5; there is no guarantee that optimizing CameraCLIP avoids reward hacking or transfers to FVD and CLIPSIM.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Can video generation replace cinematographers? Research on the cinematic language of generated video." pith.science (2026). https://pith.science/paper/N6C4LEBS

@misc{pith2026241212223,
  author       = {Pith},
  title        = {Pith review of: Can video generation replace cinematographers? Research on the cinematic language of generated video},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N6C4LEBS}},
  note         = {Machine review of arXiv:2412.12223}
}
read the original abstract

Recent advancements in text-to-video (T2V) generation have leveraged diffusion models to enhance visual coherence in videos synthesized from textual descriptions. However, existing research primarily focuses on object motion, often overlooking cinematic language, which is crucial for conveying emotion and narrative pacing in cinematography. To address this, we propose a threefold approach to improve cinematic control in T2V models. First, we introduce a meticulously annotated cinematic language dataset with twenty subcategories, covering shot framing, shot angles, and camera movements, enabling models to learn diverse cinematic styles. Second, we present CameraDiff, which employs LoRA for precise and stable cinematic control, ensuring flexible shot generation. Third, we propose CameraCLIP, designed to evaluate cinematic alignment and guide multi-shot composition. Building on CameraCLIP, we introduce CLIPLoRA, a CLIP-guided dynamic LoRA composition method that adaptively fuses multiple pre-trained cinematic LoRAs, enabling smooth transitions and seamless style blending. Experimental results demonstrate that CameraDiff ensures stable and precise cinematic control, CameraCLIP achieves an R@1 score of 0.83, and CLIPLoRA significantly enhances multi-shot composition within a single video, bridging the gap between automated video generation and professional cinematography.\textsuperscript{1}

Figures

Figures reproduced from arXiv: 2412.12223 by the authors.

Figure 1
Figure 1. Cinematic language generation results of CameraDiff. CameraDiff generates twenty types of cinematic controls, including shot framing (a), shot angles (b), and camera movements (c) such as lens shifts (e.g., rack focus, zoom in/out) and camera body movements (e.g., tilt up/down). Additionally, it enables the flexible combination of multiple camera movements (d). Abstract Recent advancements in text-to-video (T2V) gen… view at source ↗
Figure 2
Figure 2. Pipeline of our threefold approach. (a) Data processing: Stage 1—data collection and classification, Stage 2—human anno￾tation, and Stage 3—manual verification. (b) CameraCLIP training: The text encoder is trained on the last two layers, while the image encoder is trained on the last four layers. Each video is uniformly sampled into eight frames, encoded via the image encoder, and mean￾pooled to obtain video feature… view at source ↗
Figure 3
Figure 3. This dataset contains three categories of cinematic lan [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Qualitative results of single-shot generation in CameraDiff. CameraDiff enables the generation of specific cinematic language for individual shot types. The first and second rows illustrate control over shot framing and shot angles, respectively, while the third and fo…
Figure 5
Figure 5. Figure 5: Qualitative results of multi-shot composition in CameraDiff. We combine single-shot LoRAs using CLIPLoRA to achieve blending of multiple shots within a single video, with cinematic language details provided below each figure. Please open in Acrobat Reader and click the…
Figure 6
Figure 6. Figure 6: Comparison of CLIPLoRA results with other LoRA [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. Temporally Grounded Compositional Camera Motion Understanding via Geometric Knowledge Distillation

    cs.CV 2026-08 conditional novelty 7.0 of 10

    CamDistill matches direct geometric feature injection for temporally grounded, compositional camera-motion recognition while removing the 3D teacher at inference, and CamChoreo provides the first real-video benchmark ...

  2. Natural Language Camera Movement Understanding

    cs.CV 2026-07 accept novelty 6.5 of 10

    A cinematographic taxonomy, atomic real+synthetic benchmark (ACaM), and targeted-augmentation SFT let an 8B VLM outperform Gemini-3.1-Pro by 10-11% on camera-movement recognition, yet a large human gap remains.

Reference graph

Works this paper leans on

46 extracted references · 21 canonical work pages · cited by 2 Pith papers

  1. [1]

    Genetic algorithm: Reviews, implemen- tations, and applications

    Tanweer Alam, Shamimul Qamar, Amit Dixit, and Mo- hamed Benaida. Genetic algorithm: Reviews, implemen- tations, and applications. arXiv preprint arXiv:2007.12673,

    arXiv 2007

  2. [2]

    Frozen in time: A joint video and image encoder for end-to-end retrieval

    Max Bain, Arsha Nagrani, G ¨ul Varol, and Andrew Zisser- man. Frozen in time: A joint video and image encoder for end-to-end retrieval. In Proceedings of the IEEE/CVF inter- national conference on computer vision , pages 1728–1738,

  3. [3]

    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. [4]

    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. 2, 3

    2023

  5. [5]

    Collecting highly paral- lel data for paraphrase evaluation

    David Chen and William B Dolan. Collecting highly paral- lel data for paraphrase evaluation. InProceedings of the 49th annual meeting of the association for computational linguis- tics: human language technologies , pages 190–200, 2011. 3

    2011

  6. [6]

    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. 2, 3

    arXiv

  7. [7]

    Sharegpt4video: Improving video understand- ing and generation with better captions

    Lin Chen, Xilin Wei, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Bin Lin, Zhenyu Tang, et al. Sharegpt4video: Improving video understand- ing and generation with better captions. arXiv preprint arXiv:2406.04325, 2024. 3

    arXiv 2024

  8. [8]

    Vqgan-clip: Open domain image generation and editing with natural language guidance

    Katherine Crowson, Stella Biderman, Daniel Kornis, Dashiell Stander, Eric Hallahan, Louis Castricato, and Ed- ward Raff. Vqgan-clip: Open domain image generation and editing with natural language guidance. In European Con- ference on Computer Vision, pages 88–105. Springer, 2022. 2

    work page 2022

Show all 46 references
  1. [9]

    Clipdraw: Exploring text-to-drawing synthesis through language-image encoders

    Kevin Frans, Lisa Soros, and Olaf Witkowski. Clipdraw: Exploring text-to-drawing synthesis through language-image encoders. In Advances in Neural Information Processing Systems, 2022. 2

    2022

  2. [10]

    Genetic algorithms in search, optimiza- tion, and machine learning

    David E Golberg. Genetic algorithms in search, optimiza- tion, and machine learning. Addion wesley, 1989(102):36,

    1989

  3. [11]

    Open-vocabulary object detection via vision and language knowledge distillation

    Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In International Conference on Learning Representations, 2021. 2

    2021

  4. [12]

    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. In The Twelfth International Conference on Learning Representa- tio...

    2024

  5. [13]

    Cameractrl: Enabling camera control for text-to-video generation

    Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrl: Enabling camera control for text-to-video generation. arXiv preprint arXiv:2404.02101, 2024. 3

    2024 arXiv

  6. [14]

    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. 2, 3

    2020

  7. [15]

    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. 2, 3

    arXiv

  8. [16]

    Lora: Low- rank adaptation of large language models

    Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low- rank adaptation of large language models. In International Conference on Learning Representations, 2021. 3

    2021

  9. [17]

    Language-driven semantic seg- mentation

    Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven semantic seg- mentation. In International Conference on Learning Rep- resentations, 2022. 2

    2022

  10. [18]

    Llava-onevision: Easy visual task transfer

    Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Zi- wei Liu, et al. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 3

    2024 arXiv

  11. [19]

    Grounded language-image pre-training

    Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jian- wei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, page...

    2022

  12. [20]

    Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning

    Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, and Tianrui Li. Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning. Neu- rocomputing, 508:293–304, 2022. 2, 7, 8

    2022

  13. [21]

    Openvid-1m: A large-scale high-quality dataset for text-to- video generation

    Kepan Nan, Rui Xie, Penghao Zhou, Tiehan Fan, Zhen- heng Yang, Zhijie Chen, Xiang Li, Jian Yang, and Ying Tai. Openvid-1m: A large-scale high-quality dataset for text-to- video generation. arXiv preprint arXiv:2407.02371 , 2024. 3

    2024 arXiv

  14. [22]

    Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever

    Alec Radford, Jong Wook Kim, Chris Hallacy, A. Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from nat- ural language supervision. In ICML, 2021. 2, 4, 7

    2021

  15. [23]

    Hierarchical text-conditional image gen- eration with clip latents

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image gen- eration with clip latents. arXiv preprint arXiv:2204.06125,

    arXiv

  16. [24]

    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. 2, 3

    2022

  17. [25]

    S. Ryu. Merging loras. https : / / github . com / cloneofsimo/lora, 2023. Accessed: 2024-11-06. 3

    2023

  18. [26]

    Photorealistic text-to-image diffusion models with deep language understanding

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information...

    2022

  19. [27]

    Tempo- ral generative adversarial nets with singular value clipping

    Masaki Saito, Eiichi Matsumoto, and Shunta Saito. Tempo- ral generative adversarial nets with singular value clipping. In Proceedings of the IEEE international conference on com- puter vision, pages 2830–2839, 2017. 3

    2017

  20. [28]

    Cinescale: A dataset of cinematic shot scale in movies

    Mattia Savardi, Andr ´as B ´alint Kov ´acs, Alberto Signoroni, and Sergio Benini. Cinescale: A dataset of cinematic shot scale in movies. Data in Brief, 36:107002, 2021. 3, 4

    2021

  21. [29]

    Cinescale2: a dataset of cinematic camera features in movies

    Mattia Savardi, Andr ´as B ´alint Kov ´acs, Alberto Signoroni, and Sergio Benini. Cinescale2: a dataset of cinematic camera features in movies. Data in Brief, 51:109627, 2023. 3, 4

    2023

  22. [30]

    Laion-5b: An open large-scale dataset for training next generation image-text models

    Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Worts- man, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in neural in- f...

    2022

  23. [31]

    Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2

    Ivan Skorokhodov, Sergey Tulyakov, and Mohamed Elho- seiny. Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3626–3636, 2022. 3

    2022

  24. [32]

    Mocogan: Decomposing motion and content for video generation

    Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1526–1535,

  25. [33]

    To- wards accurate generative models of video: A new metric & challenges

    Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly. To- wards accurate generative models of video: A new metric & challenges. arXiv preprint arXiv:1812.01717, 2018. 7

    2018 arXiv

  26. [34]

    Clipasso: Semantically-aware object sketching

    Yael Vinker, Ehsan Pajouheshgar, Jessica Y Bo, Ro- man Christian Bachmann, Amit Haim Bermano, Daniel Cohen-Or, Amir Zamir, and Ariel Shamir. Clipasso: Semantically-aware object sketching. ACM Transactions on Graphics (TOG), 41(4):1–11, 2022. 2

    2022

  27. [35]

    Generating videos with scene dynamics

    Carl V ondrick, Hamed Pirsiavash, and Antonio Torralba. Generating videos with scene dynamics. Advances in neu- ral information processing systems, 29, 2016. 3

    2016

  28. [36]

    Cocaclip: Exploring distillation of fully- connected knowledge interaction graph for lightweight text- image retrieval

    Jiapeng Wang, Chengyu Wang, Xiaodan Wang, Jun Huang, and Lianwen Jin. Cocaclip: Exploring distillation of fully- connected knowledge interaction graph for lightweight text- image retrieval. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguist...

    2023

  29. [37]

    Videoclip-xl: Advancing long descrip- tion understanding for video clip models

    Jiapeng Wang, Chengyu Wang, Kunzhe Huang, Jun Huang, and Lianwen Jin. Videoclip-xl: Advancing long descrip- tion understanding for video clip models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Lan- guage Processing, pages 16061–16075, 2024. 2, 7

    2024

  30. [38]

    Videocomposer: Compositional video synthesis with motion controllability

    Xiang Wang, Hangjie Yuan, Shiwei Zhang, Dayou Chen, Ji- uniu Wang, Yingya Zhang, Yujun Shen, Deli Zhao, and Jin- gren Zhou. Videocomposer: Compositional video synthesis with motion controllability. Advances in Neural Information Processing Systems, 36, 2024. 2, 3

    2024

  31. [39]

    Internvid: A large-scale video-text dataset for multimodal understanding and generation

    Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinhao Li, Guo Chen, Xinyuan Chen, Yaohui Wang, et al. Internvid: A large-scale video-text dataset for multimodal understanding and generation. InThe Twelfth In- ternational Conference on Learning Representations ,...

    2023

  32. [40]

    Motionctrl: A unified and flexible motion controller for video generation

    Zhouxia Wang, Ziyang Yuan, Xintao Wang, Yaowei Li, Tianshui Chen, Menghan Xia, Ping Luo, and Ying Shan. Motionctrl: A unified and flexible motion controller for video generation. In ACM SIGGRAPH 2024 Conference Pa- pers, pages 1–11, 2024. 2, 3

    2024

  33. [41]

    Videoclip: Contrastive pre-training for zero-shot video-text understanding

    Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zero-shot video-text understanding. In Proceedings of the 2021 Conference on Empirical Methods in Natural L...

    2021

  34. [42]

    Groupvit: Semantic segmentation emerges from text supervision

    Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 18134–18144, 2022. 2

    2022

  35. [43]

    Direct-a-video: Customized video generation with user- directed camera movement and object motion

    Shiyuan Yang, Liang Hou, Haibin Huang, Chongyang Ma, Pengfei Wan, Di Zhang, Xiaodong Chen, and Jing Liao. Direct-a-video: Customized video generation with user- directed camera movement and object motion. In Special Interest Group on Computer Graphics and Interactive Tech- niq...

  36. [44]

    Long-clip: Unlocking the long-text capability of clip

    Beichen Zhang, Pan Zhang, Xiaoyi Dong, Yuhang Zang, and Jiaqi Wang. Long-clip: Unlocking the long-text capability of clip. In European Conference on Computer Vision, pages 310–325. Springer, 2025. 2, 7

    2025

  37. [45]

    Multi-lora composition for image generation

    Ming Zhong, Yelong Shen, Shuohang Wang, Yadong Lu, Yizhu Jiao, Siru Ouyang, Donghan Yu, Jiawei Han, and Weizhu Chen. Multi-lora composition for image generation. arXiv preprint arXiv:2402.16843, 2024. 3

    2024 arXiv

  38. [46]

    Stereo magnification: learning view synthesis using multiplane images

    Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: learning view synthesis using multiplane images. ACM Transactions on Graphics (TOG), 37(4):1–12, 2018. 3, 4

    2018

Pith tools

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