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arxiv: 2604.08500 · v1 · submitted 2026-04-09 · 💻 cs.CV

Recognition: no theorem link

Novel View Synthesis as Video Completion

Deva Ramanan, Khiem Vuong, Minsik Jeon, Qi Wu, Srinivasa Narasimhan

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Pith reviewed 2026-05-10 18:21 UTC · model grok-4.3

classification 💻 cs.CV
keywords novel view synthesisvideo diffusion modelsvideo completionpermutation invariancesparse multi-viewtemporal embeddingsframe ordering
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The pith

Video diffusion models can be adapted for sparse novel view synthesis by reformulating it as low frame-rate video completion and removing temporal order cues.

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

The paper sets out to show that existing video diffusion models already hold implicit knowledge of multi-view geometry, which can be activated for novel view synthesis without building new image-based priors from scratch. It does this by casting the task of predicting a target view from a few unordered input images and their poses as a kind of sparse video completion problem. The key adaptation is to make the model ignore the usual frame ordering so it treats the inputs as a permutation-invariant set rather than a timed sequence. If this holds, it means practitioners can repurpose large video models for 3D view synthesis with only light fine-tuning instead of training dedicated multi-view generators.

Core claim

Sparse novel view synthesis reduces to low frame-rate video completion once video diffusion models are modified to become invariant to input ordering; the modifications consist of per-frame latent encodings plus removal of temporal positional embeddings, after which the models can be fine-tuned with minimal supervision to produce competitive results on standard sparse-view benchmarks.

What carries the argument

FrameCrafter, the adapted video diffusion architecture that uses per-frame latent encodings and drops temporal positional embeddings to enforce permutation invariance on unordered input sets.

If this is right

  • Video models contain implicit multi-view geometric knowledge that survives the removal of temporal positional embeddings.
  • Only minimal supervision is needed to train such models to ignore frame ordering while still producing coherent novel views.
  • Sparse-view NVS performance becomes competitive with methods that rely on single-image generative priors.
  • The same adaptation pattern may allow other video-trained models to handle unordered multi-view tasks without explicit 3D supervision.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same removal of temporal embeddings could be tested on other tasks that require set-like rather than sequential inputs, such as unordered image sets for 3D reconstruction.
  • If the approach scales, it suggests that future multi-view generators might be derived from video pre-training rather than image pre-training, changing data collection priorities.
  • One could measure how much geometric consistency is retained purely by the diffusion prior versus how much is added during the light fine-tuning stage.

Load-bearing premise

Video diffusion models already encode usable multi-view geometric knowledge from their original training that survives the removal of temporal cues and can be activated by light fine-tuning on sparse inputs.

What would settle it

Train a video diffusion model on the described modifications and fine-tuning regime, then check whether the generated target views remain geometrically consistent with the provided camera poses when the input images are presented in random order; failure of consistency across random permutations would falsify the claim.

Figures

Figures reproduced from arXiv: 2604.08500 by Deva Ramanan, Khiem Vuong, Minsik Jeon, Qi Wu, Srinivasa Narasimhan.

Figure 1
Figure 1. Figure 1: Video Diffusion as Multi-view Prior. While prior generative NVS meth￾ods typically initialize from image diffusion models and rely on large pose-annotated multi-view datasets, videos naturally capture viewpoint changes and cross-view con￾sistency, making them a more scalable source of supervision for NVS. We present FrameCrafter, which adapts pretrained video diffusion models with lightweight mod￾ification… view at source ↗
Figure 2
Figure 2. Figure 2: Each input image is encoded by a frozen video VAE, and its camera pose is converted into a Plücker ray map that is concatenated with the image latent along the channel dimension. The resulting view tokens are then concatenated along the temporal dimension, where the first K tokens correspond to input views and the final token represents the query view. Only the patch embedding layer and the LoRA modules in… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison between standard causal video VAE encoding and our [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison under sparse inputs. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance on different num￾bers of training scenes. Even with only 20 scenes, our model surpasses EscherNet trained on 10K scenes. Performance im￾proves consistently as training data in￾creases, demonstrating strong data effi￾ciency and scalability. Data Scaling We further analyze the effect of training data scale on model performance ( [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Additional Qualitative Comparisons with SEVA [55], Aether [57] and LVSM [17]. serve as a form of data augmentation: each scene can be observed under many differ￾ent input orderings, exposing the model to a much richer set of training configurations. From this perspective, permutation-invariant training can be viewed as implicitly lever￾aging an exponentially larger set of permuted training samples. Our arc… view at source ↗
read the original abstract

We tackle the problem of sparse novel view synthesis (NVS) using video diffusion models; given $K$ ($\approx 5$) multi-view images of a scene and their camera poses, we predict the view from a target camera pose. Many prior approaches leverage generative image priors encoded via diffusion models. However, models trained on single images lack multi-view knowledge. We instead argue that video models already contain implicit multi-view knowledge and so should be easier to adapt for NVS. Our key insight is to formulate sparse NVS as a low frame-rate video completion task. However, one challenge is that sparse NVS is defined over an unordered set of inputs, often too sparse to admit a meaningful order, so the models should be $\textit{invariant}$ to permutations of that input set. To this end, we present FrameCrafter, which adapts video models (naturally trained with coherent frame orderings) to permutation-invariant NVS through several architectural modifications, including per-frame latent encodings and removal of temporal positional embeddings. Our results suggest that video models can be easily trained to "forget" about time with minimal supervision, producing competitive performance on sparse-view NVS benchmarks. Project page: https://frame-crafter.github.io/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes FrameCrafter, an adaptation of video diffusion models for sparse novel view synthesis (NVS). It reformulates the task as low frame-rate video completion given K≈5 unordered input views and their camera poses, with the goal of predicting a target view. Architectural changes include per-frame latent encodings and removal of temporal positional embeddings to achieve permutation invariance, allowing the model to 'forget' about time. The central claim is that video models already encode implicit multi-view knowledge and can be adapted with minimal supervision to achieve competitive results on sparse-view NVS benchmarks.

Significance. If the empirical claims hold, the work is significant because it offers a new perspective on leveraging video generative priors for multi-view geometry tasks rather than relying solely on single-image diffusion models. The reformulation as video completion and the focus on minimal supervision are strengths. Credit is due for the explicit handling of unordered inputs via the described modifications, which could influence future work on adapting temporal models to set-based tasks in computer vision.

major comments (2)
  1. [Method (description of per-frame encodings and temporal embedding removal)] The central claim of permutation invariance (and thus successful 'forgetting' of time) rests on the modifications in the FrameCrafter architecture. However, video diffusion backbones typically retain 3D convolutions, temporal attention layers, or frame-wise processing that can retain residual order dependence even after temporal positional embeddings are removed. No experiment is reported that feeds the same K views in different orders and verifies identical outputs. This directly affects whether the competitive benchmark performance can be attributed to implicit multi-view knowledge rather than order artifacts.
  2. [Abstract and Experiments section] The abstract asserts 'competitive performance on sparse-view NVS benchmarks' but the provided text contains no quantitative numbers, specific baselines, ablation tables, or error metrics. Without these details (presumably in §4), the strength of the empirical support for the central claim cannot be assessed. Please add explicit comparisons to prior NVS methods and ablations on the invariance modifications.
minor comments (2)
  1. [Method] Clarify how the target camera pose is encoded and injected into the model, as this is central to the NVS formulation but not detailed in the abstract.
  2. [Experiments] Ensure all figures showing qualitative results include the input views, target pose, and ground truth for direct comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and insightful comments. We address each major comment point by point below and describe the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Method (description of per-frame encodings and temporal embedding removal)] The central claim of permutation invariance (and thus successful 'forgetting' of time) rests on the modifications in the FrameCrafter architecture. However, video diffusion backbones typically retain 3D convolutions, temporal attention layers, or frame-wise processing that can retain residual order dependence even after temporal positional embeddings are removed. No experiment is reported that feeds the same K views in different orders and verifies identical outputs. This directly affects whether the competitive benchmark performance can be attributed to implicit multi-view knowledge rather than order artifacts.

    Authors: We agree that an explicit empirical verification of permutation invariance is valuable and would strengthen the central claim. While the per-frame latent encodings and removal of temporal positional embeddings are specifically designed to eliminate order cues (allowing the model to process inputs as an unordered set), we acknowledge that residual dependencies could potentially remain in other backbone components such as 3D convolutions or attention layers. In the revised manuscript, we will add a dedicated experiment that feeds identical sets of K input views in multiple random permutations and quantifies output consistency (e.g., via PSNR, SSIM, and LPIPS between the resulting target views). This will provide direct evidence that performance arises from implicit multi-view knowledge rather than order artifacts. revision: yes

  2. Referee: [Abstract and Experiments section] The abstract asserts 'competitive performance on sparse-view NVS benchmarks' but the provided text contains no quantitative numbers, specific baselines, ablation tables, or error metrics. Without these details (presumably in §4), the strength of the empirical support for the central claim cannot be assessed. Please add explicit comparisons to prior NVS methods and ablations on the invariance modifications.

    Authors: The full manuscript contains quantitative results and comparisons in Section 4, including evaluations on standard sparse-view NVS benchmarks (DTU and RealEstate10K), direct comparisons against prior methods (e.g., single-image diffusion baselines and dedicated NVS approaches), ablation studies isolating the effects of the per-frame encoding and temporal embedding removal, and standard error metrics (PSNR, SSIM, LPIPS). The abstract summarizes these findings at a high level without numbers, which is conventional for brevity. To make the empirical support more immediately accessible, we will revise the abstract to include a brief mention of key quantitative highlights and ensure the experiments section features clearly labeled tables for all baselines and ablations. revision: yes

Circularity Check

0 steps flagged

Empirical adaptation of video models for NVS is self-contained with no circular derivation

full rationale

The paper proposes treating sparse NVS as low frame-rate video completion and adapts video diffusion models via per-frame latent encodings plus removal of temporal positional embeddings to enforce permutation invariance. Effectiveness is measured by performance on external benchmarks rather than by any internal loss or fitted quantity. No equations, predictions, or uniqueness claims reduce to the method's own inputs by construction. The approach is presented as a practical architectural modification whose validity is established empirically, not definitionally or via self-citation chains.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that video models already contain usable multi-view knowledge; no new physical entities or free parameters are introduced in the abstract, but training hyperparameters and the exact definition of 'minimal supervision' remain unspecified.

axioms (1)
  • domain assumption Video diffusion models trained on coherent frame sequences already encode implicit multi-view geometric knowledge.
    Explicitly stated as the key insight in the abstract.

pith-pipeline@v0.9.0 · 5526 in / 1274 out tokens · 27309 ms · 2026-05-10T18:21:54.405209+00:00 · methodology

discussion (0)

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