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arxiv: 2605.00658 · v1 · submitted 2026-05-01 · 💻 cs.CV

Recognition: unknown

UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors

Houyuan Chen , Hong Li , Xianghao Kong , Tianrui Zhu , Shaocong Xu , Weiqing Xiao , Yuwei Guo , Chongjie Ye
show 3 more authors
Lvmin Zhang Hao Zhao Anyi Rao
Authors on Pith no claims yet

Pith reviewed 2026-05-09 19:59 UTC · model grok-4.3

classification 💻 cs.CV
keywords video diffusion modelsunified multimodal generationstochastic condition maskingdecoupled gated LoRAcross-modal self-attentionintrinsic map predictionalpha layer decomposition
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The pith

A single video diffusion model can handle multiple pixel-aligned tasks like intrinsic decomposition and alpha matting by randomly assigning modalities as conditions or targets while preserving original priors.

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

Video diffusion models are typically retrained from scratch for each new input-output mapping, which wastes their shared generative knowledge and prevents learning cross-modal correlations. UniVidX instead trains one backbone on several related tasks at once by randomly deciding during each step which modalities serve as clean inputs and which are noisy outputs to predict. It keeps the base model's strong video priors intact by attaching lightweight per-modality adapters that turn on only for the target modality, and it adds cross-modal attention layers that let modalities exchange information. The resulting model can therefore generate intrinsic maps from RGB video or separate blended video into RGBA layers, and it remains competitive with task-specific systems even when trained on fewer than one thousand videos.

Core claim

UniVidX formulates pixel-aligned tasks as conditional generation in a shared multimodal space, adapts to modality-specific distributions while preserving the backbone's native priors, and promotes cross-modal consistency during synthesis. It is built on three key designs: Stochastic Condition Masking randomly partitions modalities into clean conditions and noisy targets during training, enabling omni-directional conditional generation instead of fixed mappings; Decoupled Gated LoRA introduces per-modality LoRAs that are activated when a modality serves as the generation target, preserving the strong priors of the VDM; and Cross-Modal Self-Attention shares keys and values across modalities.

What carries the argument

Stochastic Condition Masking randomly partitions modalities into clean conditions and noisy targets; Decoupled Gated LoRA activates per-modality adapters only when the modality is the generation target; Cross-Modal Self-Attention shares keys and values across modalities while using modality-specific queries to enable information exchange and alignment.

If this is right

  • The same trained model supports arbitrary input-output mappings across modalities without requiring new training runs.
  • Cross-modal information exchange produces more consistent results when multiple related modalities are generated together.
  • Strong performance is retained on in-the-wild videos despite using training sets smaller than 1,000 examples.
  • The approach matches the accuracy of specialized state-of-the-art models on individual tasks such as intrinsic map prediction and alpha layer separation.

Where Pith is reading between the lines

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

  • The same adapter-based approach could be extended to additional modalities such as depth or optical flow by introducing new per-modality LoRAs.
  • Preserving the base diffusion priors may lower the data volume needed for future video-related conditional tasks in general.
  • Omni-directional generation could enable iterative pipelines in which the output of one modality task is immediately reused as input for another within the same model.

Load-bearing premise

That the three designs of stochastic masking, gated per-modality adapters, and cross-modal attention together preserve the original video diffusion model's strong generative priors while still allowing effective cross-modal information exchange and flexible conditional generation.

What would settle it

Training the unified model on the same small dataset and observing that it produces markedly worse outputs than separately trained task-specific models on any single task, or that generated outputs become inconsistent when modalities are interchanged.

Figures

Figures reproduced from arXiv: 2605.00658 by Anyi Rao, Chongjie Ye, Hao Zhao, Hong Li, Houyuan Chen, Lvmin Zhang, Shaocong Xu, Tianrui Zhu, Weiqing Xiao, Xianghao Kong, Yuwei Guo.

Figure 1
Figure 1. Figure 1: UniVidX is a unified multimodal framework designed for versatile video generation, which supports diverse paradigms (Text→X, X→X, and Text&X→X; ’X’ denotes visual modality like albedo). We instantiate this framework into two models: 1) UniVid-Intrinsic (top), which supports tasks including text-to-intrinsic, inverse rendering, and video relighting; and 2) UniVid-Alpha (bottom), which supports tasks includi… view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of UniVidX (using UniVid-Intrinsic as an exam￾ple). Multimodal inputs are encoded and passed through Stochastic Con￾dition Masking (SCM), which randomly assigns them as clean conditions or noisy targets. The DiT blocks are equipped with Decoupled Gated LoRA (DGL): distinct LoRAs are assigned to each modality and are activated only for target inputs while deactivated for conditions (indicated b… view at source ↗
Figure 3
Figure 3. Figure 3: Visual comparison for text-to-intrinsic generation. Compared to IntrinsiX, which exhibits noticeable artifacts and modality misalignment (indicated by red boxes), our UniVid-Intrinsic produces superior results. Our method generates temporally coherent video clips with precise alignment across RGB, albedo, and normal maps, effectively capturing complex geometries and fine textures like the cat’s fur. Please… view at source ↗
Figure 4
Figure 4. Figure 4: Visual results for text-to-RGBA generation. Compared to Lay￾erDiffuse, which is limited to static images, our method can generate high￾quality, dynamic RGBA videos. Notably, while LayerDiffuse needs distinct prompts for different layers to ensure separation, our method achieves ro￾bust performance using a single shared prompt. (𝛽1 = 0.9, 𝛽2 = 0.999, weight decay=10−2 ) coupled with a Cosine Annealing sched… view at source ↗
Figure 5
Figure 5. Figure 5: Visual comparison for inverse and forward rendering tasks. In all tasks, UniVid-Intrinsic produces results closest to the Ground Truth. methods against representative image generation models. For text￾to-intrinsic, we compare UniVid-Intrinsic against IntrinsiX [Kocsis et al. 2025] on the intersection of modalities: RGB, albedo, and nor￾mal. Notably, while our model generates RGB frames simultaneously with … view at source ↗
Figure 6
Figure 6. Figure 6: Normal estimation on a cinematic video sequence. Compared to specialized normal estimators and intrinsic-related baselines which struggle with temporal stability or detail preservation, our method yields temporally coherent normals while maintaining high-fidelity geometric details. details, such as the fur of the cat (see [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visual comparison of auxiliary-free video matting results. While competing approaches exhibit noticeable artifacts and background leakage (e.g., the wall sconce), our method produces accurate mattes. estimation capabilities. As shown in [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative ablation of decoupling design. Comparison of generation using distinct prompts (Left) vs. a shared prompt (Right). While LayerDiffuse fails with shared prompt and the ’w/o Dec.’ variant consistently fails due to parameter sharing, our approach achieves robust generation in both situations. Prompt: Two little kittens hold tiny teacups… Prompt: A red fox wearing a woolen coat, … Channel-Concatena… view at source ↗
Figure 9
Figure 9. Figure 9: Visual results of channel-concatenation. Left: Albedo results for text-to-intrinsic generation. Right: FG results for text-to-RGBA generation. In both tasks, the channel-concatenation variant fails completely, yielding corrupted outputs due to the disruption of the diffusion priors. Conversely, UniVid-Intrinsic and UniVid-Alpha models generate high-fidelity results, demonstrating the superiority of our Uni… view at source ↗
Figure 10
Figure 10. Figure 10: Attention map analysis. Maps are extracted from the Cross-Modal Self-Attention layers in the 20th DiT block at denoising step 25/50. Top: Our method yields clean attention maps where FG and BG branches distinctively attend to the subject and background. Bottom: The ’w/o Dec.’ variant results are noisy, proving its inability to separate different modalities effectively [PITH_FULL_IMAGE:figures/full_fig_p0… view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative ablation on Cross-Modal Self-Attention. We compare the text-to-intrinsic generation results of our UniVid-Intrinsic with the ’w/ Van.’ variant using the same prompt. As shown, our model demonstrates superior structural consistency across all modalities (RGB, albedo, irradiance, and normal). In contrast, the ’w/ Van.’ variant suffers from noticeable inconsistencies and misalignment between diff… view at source ↗
Figure 13
Figure 13. Figure 13: Demonstrating the value of multi-condition for mitigating perceptual ambiguity. The single-condition RGB input (top) fails to cap￾ture the geometry of the distant, blurry object due to the inherent ambiguity of the RGB input. In contrast, by utilizing the auxiliary Albedo modality as a structural constraint, the multi-condition RGB + albedo input (bottom) successfully reconstructs the surface normals of t… view at source ↗
Figure 14
Figure 14. Figure 14: Failure cases of our models. Top row (UniVid-Intrinsic): The inverse rendering results given the input RGB (enclosed in a red border). We observe instability in normal estimation for transparent glass surfaces: while it successfully reconstructs the claw machine’s glass (highlighted in the yellow box), it fails to capture the geometry of the central glass cover (highlighted in the green box). Bottom row (… view at source ↗
Figure 15
Figure 15. Figure 15: Application of UniVid-Intrinsic — Video Relighting. The figure illustrates a two-stage relighting pipeline. First, we perform inverse rendering on the input RGB to get albedo and normal maps. Second, using these intrinsic components as conditions along with a target text prompt, we generate the relighted RGB video and irradiance maps. The reference column displays the original input video and its irradian… view at source ↗
Figure 16
Figure 16. Figure 16: Application of UniVid-Intrinsic — Text-driven Video Retexturing. First, we generate the initial RGB and intrinsic maps from a source prompt. Second, we freeze the generated geometry (normal and irradiance) to constrain the structure, while re-synthesizing the RGB and albedo via a target prompt. This pipeline allows for surface appearance control without altering the underlying scene geometry and lighting.… view at source ↗
Figure 17
Figure 17. Figure 17: Application of UniVid-Intrinsic — Material Editing. First, the input video is decomposed into intrinsic maps. We then manually edit the albedo and normal maps. Finally, taking these edited maps and the original irradiance as conditions, UniVid-Intrinsic generates the output with edited materials. right-side wall, yet fails on the central glass cover, where the es￾timated normals erroneously penetrate the … view at source ↗
Figure 18
Figure 18. Figure 18: Application of UniVid-Alpha — Video Inpainting. First, we decompose the input video into alpha mattes and background components. Second, conditioning on these extracted alpha mattes and background videos, we generate new foreground and blended RGB videos controlled by a text prompt. This allows for precise appearance editing of the subject within the original context. Prompt Prompt: A panda is eating bamb… view at source ↗
Figure 19
Figure 19. Figure 19: Application of UniVid-Alpha — Background Replacement. We first generate the alpha matte and foreground from a text prompt. Then, conditioning on these components and a new background prompt, the model generates the new background and blended RGB output. Input BL Condition Alpha BG FG Prompt: A man with long curly hair, … the exterior wall. BL FG Alpha [PITH_FULL_IMAGE:figures/full_fig_p014_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Application of UniVid-Alpha — Foreground Replacement. We first extract the background from input video through matting. By conditioning on this background and target foreground prompt, the model synthesizes the corresponding blended RGB, foreground and alpha matte output. training data: the human-centric matting dataset VideoMatte240K lacks labels for transparent objects with semi-transparent alpha mattes… view at source ↗
read the original abstract

Recent progress has shown that video diffusion models (VDMs) can be repurposed for diverse multimodal graphics tasks. However, existing methods often train separate models for each problem setting, which fixes the input-output mapping and limits the modeling of correlations across modalities. We present UniVidX, a unified multimodal framework that leverages VDM priors for versatile video generation. UniVidX formulates pixel-aligned tasks as conditional generation in a shared multimodal space, adapts to modality-specific distributions while preserving the backbone's native priors, and promotes cross-modal consistency during synthesis. It is built on three key designs. Stochastic Condition Masking (SCM) randomly partitions modalities into clean conditions and noisy targets during training, enabling omni-directional conditional generation instead of fixed mappings. Decoupled Gated LoRA (DGL) introduces per-modality LoRAs that are activated when a modality serves as the generation target, preserving the strong priors of the VDM. Cross-Modal Self-Attention (CMSA) shares keys and values across modalities while keeping modality-specific queries, facilitating information exchange and inter-modal alignment. We instantiate UniVidX in two domains: UniVid-Intrinsic, for RGB videos and intrinsic maps including albedo, irradiance, and normal; and UniVid-Alpha, for blended RGB videos and their constituent RGBA layers. Experiments show that both models achieve performance competitive with state-of-the-art methods across distinct tasks and generalize robustly to in-the-wild scenarios, even when trained on fewer than 1,000 videos. Project page: https://houyuanchen111.github.io/UniVidX.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 introduces UniVidX, a unified multimodal framework for versatile video generation that builds upon video diffusion models (VDMs). It proposes three designs—Stochastic Condition Masking (SCM), Decoupled Gated LoRA (DGL), and Cross-Modal Self-Attention (CMSA)—to enable omni-directional conditional generation across modalities while adapting to specific distributions and maintaining cross-modal consistency. The approach is applied to intrinsic video decomposition (UniVid-Intrinsic) and alpha matting (UniVid-Alpha), with claims of competitive performance against state-of-the-art methods and robust generalization even when trained on limited data (<1000 videos).

Significance. If the results hold, this work could significantly impact the field by providing a single adaptable model for multiple pixel-aligned video tasks, efficiently utilizing existing VDM priors without requiring separate models per task. The emphasis on low-data generalization and cross-modal consistency addresses practical challenges in multimodal graphics and vision.

major comments (2)
  1. The central claim that the three designs (particularly Decoupled Gated LoRA) simultaneously preserve the backbone VDM's native priors while enabling effective cross-modal adaptation and omni-directional generation is load-bearing but lacks direct empirical isolation. No control experiments are described that re-run the frozen original VDM on its native distribution or quantify distribution shift after adaptation (e.g., via FID or negative log-likelihood on clean video frames). Competitive task performance alone does not confirm prior preservation versus task-specific override.
  2. The experiments section claims competitive performance and robust in-the-wild generalization with <1000 training videos, yet the provided abstract and summary supply no specific metrics, baselines, ablation results on the individual contributions of SCM/DGL/CMSA, or error analysis. This makes it impossible to verify the strength of the cross-modal consistency claims or the low-data advantage.
minor comments (2)
  1. The abstract would be strengthened by including at least one or two key quantitative results (e.g., PSNR, FID, or task-specific scores) alongside the qualitative claims of competitiveness.
  2. Clarify the exact training dataset sizes, video lengths, and resolution details for both UniVid-Intrinsic and UniVid-Alpha to allow reproducibility assessment.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and describe the revisions we will make to strengthen the empirical support for our claims.

read point-by-point responses

  1. Referee: The central claim that the three designs (particularly Decoupled Gated LoRA) simultaneously preserve the backbone VDM's native priors while enabling effective cross-modal adaptation and omni-directional generation is load-bearing but lacks direct empirical isolation. No control experiments are described that re-run the frozen original VDM on its native distribution or quantify distribution shift after adaptation (e.g., via FID or negative log-likelihood on clean video frames). Competitive task performance alone does not confirm prior preservation versus task-specific override.

    Authors: We agree that direct empirical isolation of prior preservation is important for supporting the central claim. While DGL is architecturally designed to activate modality-specific adapters only when a modality is the generation target (leaving the backbone largely unchanged), the manuscript does not include explicit controls such as re-running the frozen original VDM on native video distributions or quantifying shift via FID/NLL on clean frames. We will add these experiments in the revision, including side-by-side comparisons of native video generation quality before and after adaptation. revision: yes

  2. Referee: The experiments section claims competitive performance and robust in-the-wild generalization with <1000 training videos, yet the provided abstract and summary supply no specific metrics, baselines, ablation results on the individual contributions of SCM/DGL/CMSA, or error analysis. This makes it impossible to verify the strength of the cross-modal consistency claims or the low-data advantage.

    Authors: The full manuscript's experiments section reports competitive results against SOTA baselines on both UniVid-Intrinsic and UniVid-Alpha, along with some component ablations and in-the-wild generalization examples using <1000 videos. However, we acknowledge that the abstract lacks specific numerical highlights and that more detailed per-component ablations plus error analysis on cross-modal consistency would improve verifiability. We will revise the abstract to summarize key metrics and expand the experiments with additional ablations isolating SCM/DGL/CMSA contributions as well as quantitative error breakdowns. revision: yes

Circularity Check

0 steps flagged

No circularity: architectural designs evaluated empirically on external benchmarks

full rationale

The paper presents UniVidX as an engineering framework that augments existing video diffusion models with three components (SCM, DGL, CMSA) to enable omni-directional conditional generation. Preservation of native priors is asserted via the per-modality gating in DGL, while cross-modal consistency is enabled by CMSA; both are tested through competitive task performance on intrinsic maps and RGBA layers, including in-the-wild generalization with <1000 training videos. No equations appear that reduce any claimed output (e.g., generation quality or consistency) to quantities defined by the authors' own fitted parameters or by self-citation. No uniqueness theorems, ansatzes, or renamings of known results are invoked. The derivation chain consists of architectural choices plus empirical validation against SOTA baselines and is therefore self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on the assumption that video diffusion priors remain useful when adapted via the three new components; no explicit free parameters or invented physical entities are introduced in the abstract.

axioms (2)
  • domain assumption Video diffusion model priors can be preserved while adapting to new modality-specific distributions via gated LoRAs.
    This is the central premise behind the Decoupled Gated LoRA design.
  • domain assumption Randomly partitioning modalities into conditions and targets during training enables omni-directional conditional generation.
    Core assumption of Stochastic Condition Masking.

pith-pipeline@v0.9.0 · 5626 in / 1409 out tokens · 23454 ms · 2026-05-09T19:59:58.700120+00:00 · methodology

discussion (0)

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