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REVIEW 4 major objections 5 minor 1 cited by

You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read See3D teaches a multi-view diffusion model to do pose-free 3D generation from web videos, reporting state-of-the-art single- and sparse-view reconstruction without camera annotations.

desk verdict Interesting pose-free conditioning idea and a large new dataset, but the headline PSNR gains are unreliable because the evaluation applies a test-time brightness alignment to ground truth that baselines don't get. read the letter →

arxiv 2412.06699 v3 pith:3E2LXBZB submitted 2024-12-09 cs.CV

Baorui Ma , Huachen Gao , Haoge Deng , Zhengxiong Luo , Tiejun Huang , Lulu Tang , Xinlong Wang This is my paper · ORCID
classification cs.CV
keywords multi-viewdiffusionpose-freevideo3Dgenerationvisual-conditionnovelviewsynthesissparse-viewreconstructiondatasetscalingGaussiansplatting
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

See3D claims that a 3D generative model can learn camera control and multi-view consistency without any pose annotations, by training a multi-view diffusion model on large-scale Internet video. To do this, the authors build WebVi3D, a curated dataset of 16M static-scene clips (320M frames) filtered for large viewpoint variation, and design a purely 2D 'visual-condition' – a time-dependent mixture of masked target frames, added noise, and noisy latents – that substitutes for camera poses. On single-view and 3-view reconstruction benchmarks, See3D reports consistent gains over pose- or warp-conditioned baselines, including a 4.63 dB PSNR gain over ViewCrafter on Tanks-and-Temples and the best sparse-view results on LLFF, DTU, and Mip-NeRF 360. The paper argues this shows costly 3D annotations can be replaced by freely available video data, enabling open-world 3D creation to scale with the web.

What carries the argument

The load-bearing object is the visual-condition Vt = [Wt*Ct + (1−Wt)*Xt; M] (Eq. 3), a concatenation of a corrupted target view Ct, the diffusion latent Xt, and a mask M, mixed by a timestep-dependent weight Wt. Ct itself is the masked target image with time-dependent noise at a reduced schedule t' = βt (β = 0.2), which the paper uses to keep enough signal for camera control while preventing conditional-image leakage at small timesteps. This purely 2D signal replaces camera poses as the control channel. The paper couples it with a warping-based generation pipeline using monocular depth (MoGe), sparse keypoint matching (SuperPoint/LightGlue), independent per-keypoint scale-shift alignment, and locally weighted linear regression to recover dense metric depth, so that warped hints stay close enough to the training-time visual-condition distribution.

What would settle it

Run See3D on a pose-annotated test set where camera trajectories are known and measure the angular error of generated views relative to ground-truth poses: if the visual-condition genuinely encodes viewpoint, this error should be small and grow smoothly with viewpoint distance, whereas a model that only interpolates appearance would show near-chance angular errors for large viewpoint shifts. A second check: train the same architecture on the same data with the visual-condition replaced by plain masked frames; the reported PSNR gap between MV-UnPoseT and MV-UnPoseM should persist on held-out videos beyond the MVImageNet subset used in the ablation.

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Extended reading notes

Core claim

The central claim is that a multi-view diffusion model conditioned solely on a visual signal derived from pixel-space video data – random masks, time-dependent noise with t' = βt, and a schedule-dependent mixture Wt*Ct + (1−Wt)*Xt – learns to control camera movement and synthesize geometrically consistent novel views. Trained on WebVi3D, the model See3D reports zero-shot single-view PSNRs of 23.76 on Tanks-and-Temples, 25.36 on RealEstate10K, and 24.28 on CO3D, each several dB above the warped-image baseline ViewCrafter*, and sparse-view results (23.23 PSNR on LLFF, 28.04 on DTU, 17.35 on Mip-NeRF 360) that exceed diffusion-based few-shot reconstructions like CAT3D and ReconFusion. The authors further show the same model, without fine-tuning, supports open-world 3D editing by masking and reconditioning regions of a reference view. The evidence is intended to show that scaling pose-free video data raises the upper bound of 3D generation beyond what 3D-annotated datasets allow.

Load-bearing premise

The approach assumes that the time-dependent mixture of masked, noised frames and noisy latents carries enough camera-motion information for a diffusion model to synthesize correct novel views without any pose annotation, and that warped test-time images fall within the distribution of this visual-condition; the paper's main numerical comparisons also rely on ground-truth brightness alignment that is not applied to baselines.

Editorial extensions

If this is right

  • Scaling training data from pose-free video is enough to displace pose-conditional multi-view diffusion: See3D reports state-of-the-art results on single- and sparse-view benchmarks without any camera input.
  • The visual-condition generalizes to warping-based pipelines and mask-based editing without fine-tuning, covering single-view-to-3D, sparse-view 3D, and open-world 3D editing.
  • The data curation pipeline (static-scene filtering, dynamic-region rejection, viewpoint-radius filtering) can be reused to grow WebVi3D as more Internet video becomes available.
  • The iterative depth alignment (per-keypoint scale-shift plus locally weighted linear regression) corrects monocular depth errors and enables long-sequence generation along complex camera trajectories.

Reading between the lines

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

  • If the pose-free scaling result holds, 3D generation performance may come to track internet video volume rather than curated 3D asset production, a shift that would change the cost structure of training such systems.
  • The time-dependent mixture is essentially a learned weighting between a content hint and a noise prior; similar schedules could be ported to other conditional diffusion tasks where pose or dense correspondences are unavailable, such as video editing or dense tracking.
  • The test-time brightness alignment used in evaluation likely inflates PSNR-type metrics relative to baselines; a fairer comparison would re-run baselines with the same alignment or report perceptual metrics on unaligned images.
  • Because the model shuffles frames and drops time embeddings, it discards temporal order; this suggests an implicit assumption that static-scene multi-view sets are interchangeable, which may limit extension to dynamic 4D content.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. See3D proposes a pose-free multi-view diffusion model for 3D generation trained on Internet videos. The paper contributes (i) WebVi3D, a dataset of ~15.99M clips/320M frames curated from ~25.5M stock and drone videos via a four-step pipeline (dynamic-object detection, optical-flow motion filtering, and keypoint-trajectory viewpoint filtering); (ii) a visual condition V_t (Eqs. 2-3) that mixes masked, time-noised target views with the noisy latent, replacing camera-pose conditions; and (iii) a warping-based 3D generation pipeline with per-keypoint depth scale/shift alignment, locally weighted regression, and 3DGS reconstruction. The paper reports strong single-view and sparse-view numbers (e.g., 23.76 PSNR on Tanks-and-Temples vs 19.13 for the re-implemented ViewCrafter in Table 2) and claims zero-shot, open-world capability. A controlled ablation on MVImageNet (Table 3) shows near-parity between the proposed pose-free condition and a pose-conditioned warped-image model. The appendix discloses a test-time brightness-scaling protocol (Appendix C.2) that aligns the warped condition with ground truth, plus a training recipe that mixes RealEstate10K and CO3D clips and 0.5M pose-annotated samples into WebVi3D and initializes from MVDream.

Significance. If validated, the core idea is significant: replacing camera-pose conditioning with a pixel-space time-dependent signal would let 3D priors be learned from web-scale video, and WebVi3D's curation pipeline (with a human-validated 88.6% precision on the filtered set) is a useful community asset. The Table 3 ablation gives credible, controlled evidence that the time-dependent visual condition can approximate pose-conditioned warp inputs, and the paper is honest enough to disclose the disputed evaluation protocol in the appendix. At present, however, the headline quantitative claims are not supported: the test-time brightness scaling leaks ground-truth target statistics into the condition and is applied without a matched treatment for baselines; the training set overlaps two of the three single-view test benchmarks; and the pose-free recipe includes a pose-conditioned initialization and 0.5M annotated samples. These issues bear directly on the Abstract's central claim of markedly outperforming models trained on 3D datasets, so the evidence requires substantial rework before the paper's claims can be assessed.

major comments (4)
  1. [Appendix C.2 (Brightness Control); Sec. 4.1] Appendix C.2 states: 'In our evaluation experiments, brightness scaling is applied to the unmasked regions of warped images to align with ground truth, reducing the impact of brightness, and thus yielding a higher correlation between the generated content and pixel-level metrics.' The scaling factor is computed from the ground-truth target image, so at test time the visual condition (Eqs. 2-3) injected into the model carries the target's global brightness statistics, and PSNR/SSIM are then measured against that same target. This is an oracle leak: PSNR is highly sensitive to global brightness offsets (a uniform offset of 15-20 gray levels can cost several dB), so the reported margins in Table 2—e.g., 4.63 dB over the re-implemented ViewCrafter on Tanks-and-Temples—cannot be attributed to viewpoint-control accuracy. Section 4.1 does not disclose this protocol, the paper does not state that the warping-based baselines (ViewCrafter, LucidDreamer) received an equivalent adjustment, and no ablation quantifies performance without the scaling. Because the paper itself identifies PSNR as the metric that 'reflects the accuracy of viewpoint control,' this issue is load-bearing for the central quantitative claim and must be addressed with a GT-free evaluation or an identical normalization applied to all methods.
  2. [Appendix C.2 (Training Configuration); Abstract; Sec. 4.1] The training configuration mixes clips 'from datasets such as Objaverse, CO3D, RealEstate10k, MVImgNet, and DL3DV' into WebVi3D, and the model is initialized from MVDream, a pose-conditioned multi-view diffusion model. Two of the three single-view benchmarks in Table 2 (RealEstate10K and CO3D) are therefore inside the training distribution, which contradicts the 'zero-shot' characterization in the Abstract and Section 4.1 and makes the comparison to baselines that were not trained on those datasets uneven; only Tanks-and-Temples is a clean out-of-distribution benchmark among the three. In addition, the disclosed 0.5M pose-annotated supplement and the MVDream initialization mean the claim of learning 3D control 'solely' from pose-free video is stronger than the actual recipe. I request results for a WebVi3D-only variant (at least at the reduced scales used in the Section 4.3 data-proportion ablations), and a rewording of the zero-shot and pose-free claims to match what is trained.
  3. [Eqs. 2-3; Appendix C.1] The definition of the visual condition is incomplete. In Eq. 3, X_t is explicitly a noisy latent, while C_t in Eq. 2 is described as a corrupted image; their weighted sum is well-defined only if both are in the same space, but the paper never states whether C_t is VAE-encoded before the mixture, nor does it specify how many channels V_t and the mask M add to the model's first convolution. Because the entire method depends on V_t, this omitted specification blocks reproduction and should be clarified in a revision.
  4. [Sec. 4.3; Appendix C.3] The pose-free mechanism rests on three coupled components—random masking, the time-dependent noise schedule t' = f(t) = βt with β = 0.2, and the piecewise mixture W_t—but the only visual-condition ablation (Table 3) contrasts the full model against a masked-only condition (MV-UnPoseM vs MV-UnPoseT). No experiment isolates the contribution of the time-dependent noise (Eq. 2) or of the mixture (Eq. 3), and no sensitivity analysis is reported for β, t_decay_end, v_decay_end, or b, so the claimed robustness of the schedule (Section 3.2.2) is not established.
minor comments (5)
  1. [Sec. 3.1; Appendix B (User Study)] The reported '77% improvement' in Appendix B is arithmetically inconsistent: the pre-filter and post-filter 3D-aware ratios are 11.6% and 88.6%, a 77 percentage-point increase (or a ~7.6x relative increase), not a 77% improvement. Please correct the wording.
  2. [Sec. 4.1-4.2; Tables 2 and 5] The claims that the method 'outperforms all baseline methods across all metrics' (Sec. 4.1) and 'surpassed state-of-the-art reconstruction models' (Sec. 4.2) are too strong: on DTU 3-view, MuRF reports a higher SSIM (0.885 vs 0.884 in Table 2), and on LLFF 9-view, FSGS, BGGS, and CAT3D all report higher SSIM than See3D (Table 5). Please qualify the claims per dataset and per metric.
  3. [Sec. 3.2.3] The sentence 'we removed the time embedding' is ambiguous: Eq. 1 requires the diffusion timestep embedding, so the removal presumably refers to the temporal/positional embedding of the video backbone (frame ordering is shuffled anyway). Please rephrase to avoid implying the timestep embedding is dropped.
  4. [Sec. 1; Table 1] The abstract and Section 1 state that WebVi3D totals 4.41 years in duration, but Table 1's filtered hours (36.27K hours) correspond to roughly 4.14 years; please reconcile the numbers.
  5. [Appendix A (Limitations)] The limitations statement lists inference speed, lack of 4D modeling, and model scalability, but does not mention the evaluation-protocol issues (test-time ground-truth brightness alignment, benchmark overlap) that most affect the interpretation of the reported numbers; these should be acknowledged or refuted in the revision.

Circularity Check

1 steps flagged · score 6.0 of 10

Reported PSNR margins are inflated by a test-time ground-truth brightness alignment applied only to See3D; the central quantitative claim is partially circular.

  1. fitted input called prediction [Appendix C.2 (Training Details, Brightness Control) and Section 4.1 (Single View to 3D, metric statement)]
    "In our evaluation experiments, brightness scaling is applied to the unmasked regions of warped images to align with ground truth, reducing the impact of brightness, and thus yielding a higher correlation between the generated content and pixel-level metrics. ... As such, PSNR reflects the accuracy of viewpoint control provided by our proposed visual-condition in multi-view generation."

    The evaluation protocol computes a brightness scaling factor from the ground-truth target and applies it to the unmasked regions of the warped-image condition before feeding the condition into See3D. The reported PSNR and SSIM are then measured against that same ground-truth image. Because the paper itself identifies PSNR as the metric that "reflects the accuracy of viewpoint control," the score now partially measures how well the model copied the target's brightness from its own input, not how well it predicted the view. Baselines (LucidDreamer, ViewCrafter, ZeroNVS, MotionCtrl, CAT3D, etc.) receive no such ground-truth alignment, so the headline 4.63 dB gain over ViewCrafter on Tanks-and-Temples is not a controlled comparison.

full rationale

The core training formulation is not circular: Eq. 1 is a standard conditional denoising objective, and the visual-condition Vt in Eq. 3 is built from masked, noised versions of the target plus the noisy latent, which is a legitimate conditioning design rather than a tautology. The data curation pipeline and the depth-alignment warping pipeline are also self-contained. There is no load-bearing self-citation or imported uniqueness theorem; MVDream, SVD, and ViewCrafter are cited as prior architectural or baseline work, not as evidence that See3D's approach is forced. The significant circularity is confined to the evaluation protocol in Appendix C.2: a test-time brightness alignment injects ground-truth target brightness into the model's input before PSNR/SSIM are computed against that same ground truth, and this is done for See3D but not for the baselines. The paper explicitly states that this "yielding a higher correlation between the generated content and pixel-level metrics," which is an admission that the reported metric is partly manufactured. Additionally, the same appendix discloses that RealEstate10K and CO3D clips are mixed into the training set, undermining the "zero-shot" interpretation of those benchmarks; this is a data-contamination concern rather than circularity, but it compounds the reliability problem. Because the headline quantitative advantage rests on a metric whose input is aligned to the ground truth at test time, the central claim is partially circular, warranting a score of 6 rather than a lower score.

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

The method introduces no new physical or representational entity; the main load-bearing elements are the hand-set conditioning schedule, the curation thresholds, and the borrowed pretrained components. The paper's scientific contribution is empirical, so the axiom ledger records the assumptions that must hold for the empirical claims to transfer beyond the specific training setup.

free parameters (5)
  • beta (noise-level scale in f(t)) = 0.2
    Controls trade-off between camera control and robustness in Eq. 2; no sensitivity analysis is reported.
  • Wt piecewise schedule parameters = t_decay_end=300, v_decay_end=0.8, b=0.075
    Hand-set coefficients for the time-dependent mixture in Eq. 3 and Appendix C.3.
  • Data curation dynamic-score thresholds = Theta_i=0.12, Theta_c=0.35 and 0.2 in Eq. 8
    Chosen thresholds to classify videos as dynamic; no optimization or ablation on these values.
  • Trajectory radius and count thresholds = r<=20, count>40, mean_radius<5
    Thresholds for the small-viewpoint filter in Step 4 of Appendix B.
  • Keypoint count and LWLR bandwidth = 1024 keypoints; bandwidth b in Eq. 5 not specified
    Hyperparameters for pixel-wise depth alignment; b is a Gaussian kernel bandwidth with no reported value or ablation.
assumptions (4)
  • domain assumption Static scenes with camera ego-motion provide valid multi-view supervision for learning 3D priors.
    The entire data curation pipeline (Section 3.1) filters videos on this basis; if dynamic content leaks through, the 3D consistency assumption fails.
  • ad hoc to paper The time-dependent visual-condition Vt embeds camera pose information implicitly through pixel-space hints.
    Eq. 2-3 define Vt as a mixture of corrupted target views and noisy latents; the paper does not prove this signal uniquely determines viewpoint, and the evaluation partially compensates for its failure with GT brightness alignment.
  • domain assumption Pretrained components (MVDream, SVD, MoGe, RAFT, CoTracker, SuperPoint, LightGlue, Mask R-CNN) perform as needed.
    Used throughout the pipeline without re-verification: MVDream for initialization, MoGe for depth, RAFT for flow, CoTracker for tracking, etc.
  • domain assumption The diffusion training objective (Eq. 1) with classifier-free guidance and a linear noise schedule is sufficient to learn multi-view consistency from unposed frames.
    Architecture and training choices are borrowed from prior MVD models (MVDream, SVD) without a dedicated analysis of why they remain valid for the new visual condition.

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Cite this review

Pith. "Pith review of You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale." pith.science (2026). https://pith.science/paper/3E2LXBZB

@misc{pith2026241206699,
  author       = {Pith},
  title        = {Pith review of: You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3E2LXBZB}},
  note         = {Machine review of arXiv:2412.06699}
}
read the original abstract

Recent 3D generation models typically rely on limited-scale 3D `gold-labels' or 2D diffusion priors for 3D content creation. However, their performance is upper-bounded by constrained 3D priors due to the lack of scalable learning paradigms. In this work, we present See3D, a visual-conditional multi-view diffusion model trained on large-scale Internet videos for open-world 3D creation. The model aims to Get 3D knowledge by solely Seeing the visual contents from the vast and rapidly growing video data -- You See it, You Got it. To achieve this, we first scale up the training data using a proposed data curation pipeline that automatically filters out multi-view inconsistencies and insufficient observations from source videos. This results in a high-quality, richly diverse, large-scale dataset of multi-view images, termed WebVi3D, containing 320M frames from 16M video clips. Nevertheless, learning generic 3D priors from videos without explicit 3D geometry or camera pose annotations is nontrivial, and annotating poses for web-scale videos is prohibitively expensive. To eliminate the need for pose conditions, we introduce an innovative visual-condition - a purely 2D-inductive visual signal generated by adding time-dependent noise to the masked video data. Finally, we introduce a novel visual-conditional 3D generation framework by integrating See3D into a warping-based pipeline for high-fidelity 3D generation. Our numerical and visual comparisons on single and sparse reconstruction benchmarks show that See3D, trained on cost-effective and scalable video data, achieves notable zero-shot and open-world generation capabilities, markedly outperforming models trained on costly and constrained 3D datasets. Please refer to our project page at: https://vision.baai.ac.cn/see3d

Figures

Figures reproduced from arXiv: 2412.06699 by the authors.

Figure 1
Figure 1. Benefiting from the proposed web-scale dataset WebVi3D, [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of See3D. (a) We propose a four-step data curation pipeline to select multi-view images from Internet videos, forming the WebVi3D dataset, which includes ∼16M video clips across diverse categories and concepts. (b) Given multiple views, we corrupt the original data into corrupted images c i t at timestep t by applying random masks and time-dependent noise. We then reweight the guidance of c i t and the nois… view at source ↗
Figure 3
Figure 3. (a-Row1): Dynamic content modifies scene geometry [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: See3D for Multi-View Generation: From iteratively generated views (brown camera), we randomly select a few anchor views (yellow stars) to guide the generation of target views along the gray camera trajectory. Keypoint matching is first performed to establish correspond…
Figure 5
Figure 5. Figure 5: Top: Qualitative ablation of visual-condition; Bottom: As timestep decreases, visualize the trend of visual-condition. ues of 19.32, 21.04, 22.57, 24.08, and 25.01, respectively. Additionally, training with unfiltered data results in gener￾ated content that often exhib…
Figure 6
Figure 6. Figure 6: Qualitative Comparison of Single/Sparse View Generation. The top three rows are results with a single view input. The bottom two rows are novel view renderings from 3DGS, where Ours is trained on dense multi-view generation given 3 views as input. Our method outperform…
Figure 7
Figure 7. Figure 7: Single-view to 3D. Compared with LucidDreamer [12] and ViewCrafter [121], which are also conditioned on warped images, our model can consistently generate high-fidelity views with detailed texture and structural information. for acceleration. In accordance with prior w…
Figure 8
Figure 8. Figure 8: Sparse-views to 3D. Given 3 input views, our model generates clear, high-fidelity novel views that closely match the ground truth (GT), without artifacts or blurring. Note that the results from DepthSplat [114] are cropped and resized following the same data processing…
Figure 9
Figure 9. Figure 9: Examples of Open-world 3D Editing. (a) Occlusion-free Editing: An Asian-style attic is added, and novel views are generated realistically. (b) Full Replacement Editing: A vase is replaced with a toy fox, seamlessly integrated into the scene from various viewpoints. (c)…
Figure 10
Figure 10. Figure 10: Piecewise Function Wt, showing linear decay for timesteps t between 300 and 1000, and a monotonically decreasing concave behavior for t < 300. Formulation for Wt. Recapping Eq.3 from the main manuscript, Vt = [Wt ∗ Ct + (1−Wt) ∗ Xt; M], where Wt is defined as a piecew…
Figure 11
Figure 11. Figure 11: Ablation on Pixel-level Depth Alignment. Model LPIPS ↓ PSNR ↑ SSIM ↑ MV-UnPoseT 0.194 25.56 0.811 MV-UnPoseT-10% 0.187 25.95 0.817 MV-UnPoseT-20% 0.183 26.19 0.820 MV-UnPoseT-60% 0.181 26.14 0.819 MV-Posed 0.182 26.21 0.822 [PITH_FULL_IMAGE:figures/full_fig_p025_11.png]
Figure 12
Figure 12. Figure 12: Examples of Long-sequence Generation. High-quality novel views generated along complex camera trajectories, maintaining spatial consistency and visual realism across extended sequences. Intuitively, models trained entirely on 3D data tend to achieve optimal performanc…
Figure 13
Figure 13. Figure 13: More Examples of Long-sequence Generation. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_13.png]

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Pith tools

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