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arxiv: 2607.05376 · v1 · pith:KWI66S6G · submitted 2026-07-06 · cs.CV · cs.GR

MV-Forcing: Long Multi-View Video Generation via 4D-Grounded Spatio-Temporal Self-Forcing

Gal Fiebelman , Hadar Averbuch-Elor , Sagie Benaim This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-07-07 13:44 UTCglm-5.2pith:KWI66S6Grecord.jsonopen to challenge →

classification cs.CV cs.GR
keywords modelgenerationmulti-viewtemporalautoregressiondiffusionlongmv-forcing
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The pith

4D reconstruction bridge unlocks long multi-view video

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

The paper presents MV-Forcing, a framework that generates videos across multiple camera viewpoints and unbounded temporal length within a single diffusion model. The core problem is that existing methods either generate long single-view videos (via temporal autoregression) or short multi-view clips (via dense bidirectional attention across all views and frames), but cannot do both simultaneously. Dense all-to-all attention across the full time-view grid scales quadratically and prevents streaming, while naive sequential view generation without geometric anchoring drifts. MV-Forcing's key insight is that an autoregressive 3D reconstruction model (CUT3R) can serve as a geometric bridge between sequentially generated views. After completing one viewpoint, the model reconstructs its 3D structure into a persistent state, queries that state from the next target camera to render a geometric prior, and feeds this prior into the diffusion model to guide generation of the next view. This replaces dense cross-view attention with a recurrent geometric memory that accumulates information from all previously generated views and timesteps. The framework extends Self-Forcing (which addresses exposure bias in temporal autoregression) to the view dimension by unrolling view-sequential generation during training and applying a distribution-matching distillation loss against the self-generated outputs. A joint denoising regime where both view slots start from noise unifies text-to-video generation of the first view with view-sequential conditioning for subsequent views. Experiments show the method matches a bidirectional teacher on short sequences while outperforming composed baselines on long sequences, with cross-view metrics remaining stable up to 5 views and 648 frames.

Core claim

An autoregressive 4D reconstruction model with a persistent latent state can replace dense bidirectional cross-view attention in multi-view video diffusion, serving as a geometric bridge that grounds each sequentially generated viewpoint in the accumulated 3D structure of all prior views. This enables both temporal and view-wise autoregression within a single few-step student model, producing geometrically consistent multi-view videos at arbitrary lengths and viewpoint counts.

What carries the argument

The central mechanism is the feedback loop between generation and reconstruction: each generated view is decoded to pixels and fed into CUT3R's recurrent state, which accumulates 4D scene structure; this state is then queried from the next target camera via raymaps to produce an RGB rendering plus a per-pixel confidence map, which a zero-initialized 3D convolution layer projects into the diffusion model's token space as conditioning. This is paired with spatio-temporal self-forcing, which unrolls autoregressive generation along both the temporal and view axes during training and applies a distribution-matching distillation loss against the bidirectional teacher's score function on the self-g

If this is right

  • Multi-view video generation could become a streaming process rather than a fixed-window batch computation, enabling real-time applications like VR rendering or interactive simulation that require continuous multi-perspective output.
  • The CUT3R-as-bridge pattern could generalize to other reconstruction models and generation backbones, suggesting that any feed-forward 3D reconstructor with a persistent, queryable state could serve as a geometric memory for autoregressive generation.
  • The joint denoising regime that unifies first-view text-to-video with view-sequential conditioning suggests a path toward models that gracefully handle arbitrary generation orders over the time-view grid, not just row-major or column-major traversal.
  • The stable cross-view metrics at 5 views and 648 frames suggest the geometric prior scales better than attention-based alternatives, which degrade quadratically, potentially making long multi-view generation practical on commodity hardware (23 GB VRAM across all configurations).

Load-bearing premise

The framework depends entirely on CUT3R producing accurate 3D reconstructions from sequentially generated, imperfect views. The paper acknowledges failure cases where extreme motion causes CUT3R to reconstruct incorrect limb geometry and extreme camera displacement causes depth ambiguity, but does not quantify how reconstruction errors accumulate over long view chains or temporal horizons.

What would settle it

If CUT3R's reconstruction error grows super-linearly with the number of accumulated views or timesteps, the geometric prior fed into the diffusion model would progressively degrade, causing cross-view consistency to collapse at large view counts or temporal lengths rather than remaining stable as reported.

Figures

Figures reproduced from arXiv: 2607.05376 by Gal Fiebelman, Hadar Averbuch-Elor, Sagie Benaim.

Figure 1
Figure 1. Figure 1: Given a text prompt and camera sequences, MV-Forcing generates coherent video across an arbitrary number of viewpoints and unbounded temporal horizons. For each prompt, we show 3 views at increasing camera displacements (rows) across 160 timesteps (columns). Appearance, motion, and scene geometry remain consistent across all views and timesteps, demonstrating the effectiveness of our 4D-grounded geometric … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of MV-Forcing. Given a text prompt, camera sequences camk−1 and camk, and a previously generated view zˆk−1, the causal student generates the next view zˆk. The decoded previous view Vk−1 = D(ˆzk−1) is integrated into CUT3R’s persistent state S, which is queried using camk to produce a geometric rendering Vˆk. This rendering is encoded and projected via a Conv3d layer into a conditioning tensor gk… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative Comparison on Long Sequences. [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: CUT3R Geometric Prior Visualization. We generate a 3-view video for the prompt “A child dances in a restaurant” and query view 2’s camera at the same timestep under two accumulation stages. For each stage, we show the rendered RGB image from CUT3R’s [35] accumulated state and the corresponding per-pixel confi￾dence map, where brighter values indicate higher reconstruction certainty. After only view 0, the … view at source ↗
Figure 5
Figure 5. Figure 5: Failure Case Analysis. Top: Generation backbone quality. When the first view V0 contains artifacts such as malformed hands (left, red boxes), these are faithfully propagated to subsequent views through the autoregressive formulation. When V0 is artifact-free (right), subsequent views remain clean. Middle: The artifact-free case extends across both views and timesteps, with consistent hand geometry maintain… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative Comparison on Short Sequences. [PITH_FULL_IMAGE:figures/full_fig_p025_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Synthetic Long Sequence Comparison. We generate 3 views across 162 frames for the prompt “A man practicing martial arts in an urban setting with tall buildings in the background.” For each method, we show 3 views (rows) at 5 timesteps (columns). All methods are trained and evaluated on the synthetic SynCamVideo dataset for a fair in-domain comparison. SF(ft)+ReCamMaster de￾grades beyond its 81-frame window… view at source ↗
Figure 8
Figure 8. Figure 8: Component Ablation. We show 3 views at a single timestep for each ablation variant described in Sec. 4.2 in the main paper. Red boxes highlight cross-view incon￾sistencies. Without view unrolling, background elements shift unnaturally across views. Without CUT3R, the occluded background behind the person changes between view￾points. Without accumulation, the tree behind the person shifts forward and change… view at source ↗
Figure 9
Figure 9. Figure 9: View Scaling. We show all generated views at a single timestep as the number of views increases from 2 to 5 at a fixed 81 frames for the prompt “A child in a green t-shirt dancing in a modern living room.” The accumulated geometric prior grounds each new view in the full 4D structure observed so far, maintaining consistent scene geometry as the view count grows [PITH_FULL_IMAGE:figures/full_fig_p028_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Temporal Scaling. We show 2 views at evenly spaced relative positions as the temporal length increases from 81 to 648 frames for the prompt “A man in a pink t-shirt practicing martial arts on a wooden deck by the ocean.”. Cross-view synchronization remains stable across an 8× increase in duration [PITH_FULL_IMAGE:figures/full_fig_p029_10.png] view at source ↗
read the original abstract

Recent advances in video diffusion models have enabled either long single-view generation through temporal autoregression, or short multi-view synthesis through bidirectional attention. However, generating long, multi-view consistent videos of dynamic scenes remains unsolved. In this work, we present MV-Forcing, a framework that composes temporal and view-wise autoregression within a single diffusion model by introducing a 4D geometric bridge between sequentially generated views. Our key insight is that an autoregressive 3D reconstruction model naturally interfaces between autoregressively generated views. Given a completed source view, we reconstruct its 3D structure and render a geometric prior of the next target viewpoint, which the diffusion model refines into a high-quality video. To extend generation beyond the teacher's fixed temporal window, we introduce a joint denoising regime where both view slots are initialized from noise during training, enabling temporally unbounded generation. We distill the model via Distribution Matching Distillation with Spatio-Temporal Self-Forcing, closing the train-inference exposure bias gap for both temporal and view-sequential autoregression. Extensive experiments on both synthetic and real-world data demonstrate that MV-Forcing produces geometrically consistent multi-view videos of dynamic scenes at arbitrary lengths and viewpoint counts using a single few-step student model.

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

3 major / 8 minor

Summary. This paper presents MV-Forcing, a framework for long multi-view video generation that composes temporal and view-wise autoregression within a single diffusion model. The key innovation is using CUT3R, a recurrent 3D reconstruction model, as a geometric bridge between sequentially generated views: given a completed source view, the model reconstructs its 3D structure, renders a geometric prior for the next target viewpoint, and conditions the diffusion model on this prior via a Conv3d layer. The framework extends Self-Forcing to the spatio-temporal domain by unrolling view-sequential generation during training (Spatio-Temporal Self-Forcing) and introduces a joint denoising regime where both view slots are initialized from noise, enabling text-to-multi-view generation. The model is distilled from a bidirectional SynCamMaster teacher via Distribution Matching Distillation (DMD). Experiments are conducted on both synthetic and real-world data, comparing against constructed baselines (Self-Forcing + ReCamMaster) and the SynCamMaster teacher, with ablations isolating each component and scaling analyses along the view and temporal axes.

Significance. The paper addresses a genuinely unsolved problem: generating long, multi-view consistent videos of dynamic scenes. The approach of replacing dense bidirectional cross-view attention with an autoregressive 4D geometric bridge (CUT3R) is architecturally novel and enables streaming inference with constant memory regardless of output length or view count. The framework is well-motivated, and the composition of temporal and view-sequential autoregression within a unified self-forcing paradigm is a non-trivial technical contribution. The authors provide ablations isolating each design choice (Tab. 3), scaling analyses along both axes independently (Tabs. 4-5), and an honest failure case analysis (Fig. 5, Sec. C). The constant peak VRAM of 23 GB across all configurations (Tab. 6) is a notable practical strength. The code and models are stated to be publicly available, supporting reproducibility.

major comments (3)
  1. Sec. 3.2, final paragraph: The Spatio-Temporal Self-Forcing mechanism (contribution #3) is designed to close the view-sequential exposure bias by unrolling autoregressive generation during training. However, due to GPU memory constraints, training only unrolls from z_0^gt to generate ẑ_1 and ẑ_2 — a single view transition. At inference, the paper claims 'arbitrary viewpoint counts' (Sec. 1, contributions). The self-forcing thus only teaches the model to handle its own errors over one view transition, not the 3+ transitions required for 5-view generation (Tab. 4). While Tab. 4 shows stable metrics up to 5 views at 81 frames, this is the shortest temporal setting and uses synthetic data only. The concern is whether error compounding along the view axis is adequately mitigated by training on only a single transition. The authors should discuss this train-inference gap more explicitly and,if
  2. Sec. 4.2, Tabs. 4-5: The headline claim is 'arbitrary lengths AND viewpoint counts' simultaneously (Sec. 1; Fig. 1 shows 3 views × 160 timesteps). However, the scaling analyses test each axis independently: Tab. 4 varies views (2→5) at fixed 81 frames, and Tab. 5 varies temporal length (81→648) at fixed 2 views. The combined stress regime — many views AND long temporal horizons — is never evaluated. Since CUT3R's persistent state accumulates along both axes, error compounding could be superlinear when both axes are extended simultaneously. The paper should either (a) provide at least one evaluation in the combined regime (e.g., 5 views × 324+ frames) or (b) explicitly scope the claim to note that the combined regime is not directly evaluated and discuss the risk.
  3. Sec. 4.1, Tab. 1: MV-Forcing is compared only against the SynCamMaster teacher at 2 views / 81 frames. The paper states that 'no existing method addresses long multi-view video generation' and constructs baselines by composing Self-Forcing and ReCamMaster (Sec. 4.1). While this composition is reasonable, the absence of comparison against any other multi-view video method (e.g., CVD [20], SV4D [38], CAT4D [37]) — even in settings where those methods could operate — weakens the competitive assessment. The authors should clarify whether these methods are truly inapplicable in the evaluated settings, or whether the comparison was omitted for scope reasons. If the latter, at least one additional comparison would strengthen the evaluation.
minor comments (8)
  1. Sec. 3.2, Eq. (3): The blockwise causal mask M_{i,j} is defined but the temporal block size K is not specified in the main text. It appears only in Sec. B.1 (K=3 latent frames). Consider stating K in Sec. 3.1 or 3.2 for self-containedness.
  2. Sec. 3.3, Eq. (10): The notation D' = C_latent + 1 is introduced but C_latent is not defined. Presumably this is the number of VAE latent channels, but this should be stated explicitly.
  3. Tab. 2: The 'Mat. Pix.' column header is abbreviated without prior definition in the main text. It is defined in Sec. B.5 as 'matching pixels with GIM confidence above 0.01,' but readers of the main text may find the abbreviation unclear. Consider adding a footnote or brief definition at first use in Sec. 4.1.
  4. Sec. 4.2, Tab. 3: The 'w/o View Unrolling' ablation shows a large degradation (FVD 1791 vs. 1560), which the paper attributes to train-inference distribution mismatch. It would strengthen the ablation to briefly describe what 'standard DMD along the view axis' means concretely — does the student condition on ground-truth preceding views during training, or is the DMD loss simply applied without unrolling?
  5. Fig. 2: The overview diagram is informative but dense. The red arrow illustrating view-sequential unrolling is mentioned in the caption but is somewhat hard to trace visually. Consider enlarging or annotating this path more prominently.
  6. Sec. B.2: The real-world finetuning uses only 500 iterations on the Mixkit subset. The paper should comment on whether this is sufficient for convergence, or whether metrics are sensitive to the number of finetuning iterations.
  7. Sec. 4.3, limitation 3: The paper notes that 'the DMD supervision never directly supervises consistency across more than two simultaneous views.' This is an important architectural limitation that could be elevated to the main text (Sec. 3.2) rather than only appearing in limitations, as it directly affects the scalability claim.
  8. References: Several arXiv preprints are cited without venue information (e.g., [4, 5, 6, 11, 23, 39]). Where published versions exist, they should be updated.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thorough and constructive report. The referee correctly identifies that the combined stress regime (many views AND long temporal horizons) is not directly evaluated, that the train-inference gap along the view axis is only partially addressed by single-transition unrolling, and that the competitive assessment could be strengthened by comparison against additional multi-view video methods. We address each point below and commit to revisions accordingly.

read point-by-point responses

  1. Referee: Sec. 3.2, final paragraph: The Spatio-Temporal Self-Forcing mechanism is designed to close the view-sequential exposure bias by unrolling autoregressive generation during training. However, due to GPU memory constraints, training only unrolls from z_0^gt to generate ẑ_1 and ẑ_2 — a single view transition. At inference, the paper claims 'arbitrary viewpoint counts.' The self-forcing thus only teaches the model to handle its own errors over one view transition, not the 3+ transitions required for 5-view generation. The authors should discuss this train-inference gap more explicitly.

    Authors: The referee raises a valid concern. We agree that the gap between single-transition unrolling during training and multi-transition chaining at inference is an important limitation that should be discussed more explicitly. We will add a dedicated paragraph in Sec. 3.2 acknowledging this gap and explaining why it is partially mitigated: (1) the DMD loss is applied to each consecutive view pair independently, so the distillation objective decomposes over view transitions, meaning the student learns to handle its own errors for each pairwise transition even though only one transition is unrolled; (2) the accumulated CUT3R geometric prior provides an external grounding signal that does not degrade with view chain length, as each new view is conditioned on the full 4D state rather than only the immediately preceding view; (3) Tab. 4 empirically shows stable metrics up to 5 views, though we agree this is at the shortest temporal setting and on synthetic data only. We will also note in Sec. 4.3 (Limitations) that extending the unrolling to longer view chains during training is a natural direction for future improvement, and that the current single-transition unrolling is a practical compromise given GPU memory constraints. We will temper the 'arbitrary viewpoint counts' claim in the abstract and contributions to clarify that this is demonstrated empirically up to 5 views and that training unrolling is limited to a single transition. revision: partial

  2. Referee: Sec. 4.2, Tabs. 4-5: The headline claim is 'arbitrary lengths AND viewpoint counts' simultaneously. However, the scaling analyses test each axis independently: Tab. 4 varies views at fixed 81 frames, and Tab. 5 varies temporal length at fixed 2 views. The combined stress regime — many views AND long temporal horizons — is never evaluated. Since CUT3R's persistent state accumulates along both axes, error compounding could be superlinear when both axes are extended simultaneously. The paper should either (a) provide at least one evaluation in the combined regime or (b) explicitly scope the claim.

    Authors: This is a fair and well-taken point. We acknowledge that the combined regime is not directly evaluated in the current manuscript. We will address this in two ways. First, we will run at least one evaluation in the combined stress regime (e.g., 5 views × 324 frames or 3 views × 648 frames) and report the results in a new table or extend Tab. 4/5. We note that Tab. 6 already reports wall-clock time and peak VRAM for a 5 views × 648 frames configuration, confirming the system runs at constant 23 GB VRAM, but we have not reported quality metrics for this configuration. We will add these. Second, regardless of the outcome, we will explicitly scope the claim in the abstract, introduction, and contributions to note that the combined regime is evaluated separately along each axis and that the joint stress test is added in the revision. We agree with the referee that error compounding could be superlinear when both axes are extended simultaneously, and we will discuss this risk transparently. revision: yes

  3. Referee: Sec. 4.1, Tab. 1: MV-Forcing is compared only against the SynCamMaster teacher at 2 views / 81 frames. The absence of comparison against any other multi-view video method (e.g., CVD, SV4D, CAT4D) — even in settings where those methods could operate — weakens the competitive assessment. The authors should clarify whether these methods are truly inapplicable in the evaluated settings, or whether the comparison was omitted for scope reasons.

    Authors: We appreciate this comment and agree that clarifying the applicability of other multi-view methods is important. The methods cited by the referee operate in settings that are fundamentally different from ours: SV4D, SV4D 2.0, and CAT4D are object-centric, generating dense orbital views of single objects with fixed camera configurations, and cannot handle open-world scenes with arbitrary camera trajectories as evaluated in our setting. CVD adds epipolar attention across views but operates on short, fixed-length clips and, to our knowledge, does not have publicly available weights or a standard evaluation protocol that would enable a fair comparison. We will add a clarifying paragraph in Sec. 4.1 explaining why these methods are not directly comparable in our evaluated settings. That said, we agree that at least one additional comparison would strengthen the evaluation. We will attempt to adapt CVD or another applicable method to our evaluation setting if feasible within the revision period, or, if this proves impractical due to code availability or domain mismatch, we will state this explicitly and provide a detailed discussion of the differences in capability and scope between our method and these approaches. revision: partial

Circularity Check

0 steps flagged

No significant circularity found. The derivation chain is self-contained against external benchmarks.

full rationale

The paper's core derivation chain is built on externally sourced, independently developed components: SynCamMaster [2] as the frozen bidirectional teacher, Self-Forcing [16] as the temporal autoregression paradigm, CUT3R [35] as a frozen external reconstruction model, and DMD [42,43] as the distillation objective. The DMD loss (Eq. 4) uses the teacher's score function as data score — standard distillation practice, not circular reasoning. The view-sequential unrolling (Eqs. 6-7) trains on the student's own generated outputs to close the train-inference exposure bias gap; this is a legitimate training strategy (following Self-Forcing's established methodology), not a self-definitional loop. The geometric prior (Eqs. 8-11) is produced by the external CUT3R model from decoded generated views — an autoregressive feedback process at inference, but not circular reasoning. The scaling 'predictions' in Tabs. 4-5 (view counts 2→5, temporal length 81→648) are genuine extrapolation tests beyond the training configuration, not fitted quantities renamed as predictions. The one self-citation [7] (Fiebelman, Averbuch-Elor, Benaim) appears in the related work as a reference for physics-guided score distillation and is not load-bearing for any central claim. The skeptic's concerns about train-inference gaps in view unrolling and untested combined stress regimes are correctness/robustness risks, not circularity. Score 1 reflects the minor, non-load-bearing self-citation.

Axiom & Free-Parameter Ledger

8 free parameters · 5 axioms · 3 invented entities

The free parameters are standard training hyperparameters (learning rates, iteration counts, block sizes) rather than fitted physical constants. The axioms are domain assumptions about the reliability of CUT3R, the generalization of pairwise to multi-view consistency, and the train-inference gap for view chains. No invented physical entities or postulated particles; the 'invented entities' are training regimes and conditioning pathways, all of which are ablated except joint view denoising.

free parameters (8)
  • temporal block size K = 3
    Controls the blockwise causal attention mask (Eq. 3). Set to K=3 latent frames; not tuned or justified beyond this choice.
  • joint denoising probability p = 0.3
    Probability of initializing both view slots from noise during training (Sec. 3.2). Set to p=0.3 for synthetic, p=0 for real-world. Not ablated.
  • ODE initialization iterations = 3000
    Number of ODE solution pair training iterations before DMD (Sec. B.1).
  • DMD training iterations = 1600
    Number of DMD distillation iterations (Sec. B.1).
  • real-world finetuning iterations = 500
    Student finetuning iterations on real-world data (Sec. B.2). Small number that may limit generalization.
  • generator learning rate = 2e-6
    DMD generator learning rate (Sec. B.1).
  • critic learning rate = 4e-7
    DMD critic learning rate (Sec. B.1).
  • denoising steps Q = 4
    Few-step denoising schedule at inference (Sec. B.4 mentions 4-step).
axioms (5)
  • domain assumption CUT3R provides sufficiently accurate 3D reconstructions from generated (imperfect) views to serve as a reliable geometric prior
    Sec. 3.3 introduces CUT3R as the geometric bridge. The entire framework depends on CUT3R's reconstructions being accurate enough to condition generation. The paper acknowledges this fails under extreme motion and camera displacement (Sec. C, Fig. 5).
  • domain assumption Pairwise view consistency (supervised by 2-view teacher) generalizes to multi-view consistency (3+ views) at inference
    Sec. 4.3: 'the SynCamMaster teacher generates only two views at a time, the DMD supervision never directly supervises consistency across more than two simultaneous views.' The model chains pairwise transitions, and Tab. 4 shows this works up to 5 views, but the generalization is an assumption.
  • domain assumption Self-forcing unrolling over 2 views during training generalizes to longer view chains at inference
    Sec. 3.2: 'In practice, due to GPU memory constraints, we unroll from z_gt_0 to generate ẑ_1 and ẑ_2.' Training unrolls 2 views; inference generates 5+ views (Tab. 4). The train-inference gap for longer chains is not directly addressed.
  • standard math Distribution Matching Distillation (DMD) with asymmetric teacher-student distillation produces a faithful few-step approximation
    DMD [42,43] and CausVid [44] are established methods. The paper applies standard DMD formulation (Eq. 4).
  • domain assumption The 3D VAE encoder/decoder (from Wan2.1) provides sufficient latent-to-pixel fidelity for CUT3R to reconstruct from decoded frames
    Sec. 3.3: decoded views V_j = D(z_j) are fed into CUT3R. VAE reconstruction quality limits CUT3R input quality.
invented entities (3)
  • Spatio-temporal self-forcing training regime independent evidence
    purpose: Extends self-forcing to the view dimension by unrolling view-sequential autoregressive generation during DMD training
    The ablation in Tab. 3 (w/o View Unrolling) shows a large degradation (FVD 1791.40 vs 1560.54, Mat. Pix. 159.73 vs 243.63), providing falsifiable evidence that the component matters.
  • 4D-grounded geometric prior via CUT3R state accumulation independent evidence
    purpose: Bridges temporal and view-sequential generation with explicit geometric conditioning from accumulated 3D reconstruction
    Ablated in Tab. 3 (w/o CUT3R, w/o Accumulation, Manual Rendering) with measurable metric changes. The geometric prior is a conditioning pathway, not a new physical entity.
  • Joint view denoising training regime no independent evidence
    purpose: Unifies first-view text-to-video generation and view-sequential conditioning within a single architecture by stochastically initializing both view slots from noise
    Not directly ablated as a separate component. The probability p is set to 0.3 without sensitivity analysis.

pith-pipeline@v1.1.0-glm · 22628 in / 3982 out tokens · 423669 ms · 2026-07-07T13:44:13.897943+00:00 · methodology

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