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ARXIV:2604.01761 · GENERATIVE VIDEO · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.01761GENERATIVE VIDEOSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALEEdoardo A. Dominici · Thomas Deixelberger · Konstantinos Vardis · Markus Steinberger · arXiv
Control image-to-video diffusion models for tasks like domain transfer and 3D scene generation by conditioning on disentangled appearance features.
Opportunity summary
Pain Control image-to-video diffusion models for tasks like domain transfer and 3D scene generation by conditioning on disentangled appearance features.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Control image-to-video diffusion models for tasks like domain transfer and 3D scene generation by conditioning on disentangled appearance features. Many applications in generation and transfer rely on conditioning these models, typically through perceptual, geometric,…
Video models have recently been applied with success to problems in content generation, novel view synthesis, and, more broadly, world simulation. Many applications in generation and transfer rely on conditioning these models, typically through…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this paper, we show how we can use the features for tasks such as video domain transfer and video-from-3D generation. Code availability is…
Generative Video moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Control image-to-video diffusion models for tasks like domain transfer and 3D scene generation by conditioning on disentangled appearance features.
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10.48550/arXiv.2604.01761Control image-to-video diffusion models for tasks like domain transfer and 3D scene generation by conditioning on disentangled appearance features.
Abstract
Video models have recently been applied with success to problems in content generation, novel view synthesis, and, more broadly, world simulation. Many applications in generation and transfer rely on conditioning these models, typically through perceptual, geometric, or simple semantic signals, fundamentally using them as generative renderers. At the same time, high-dimensional features obtained from large-scale self-supervised learning on images or point clouds are increasingly used as a general-purpose interface for vision models. The connection between the two has been explored for subject specific editing, aligning and training video diffusion models, but not in the role of a more general conditioning signal for pretrained video diffusion models. Features obtained through self-supervised learning like DINO, contain a lot of entangled information about style, lighting and semantics of the scene. This makes them great at reconstruction tasks but limits their generative capabilities. In this paper, we show how we can use the features for tasks such as video domain transfer and video-from-3D generation. We introduce a lightweight architecture and training strategy that decouples appearance from other features that we wish to preserve, enabling robust control for appearance changes such as stylization and relighting. Furthermore, we show that low spatial resolution can be compensated by higher feature dimensionality, improving controllability in generative rendering from explicit spatial representations.
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PROBLEM
Control image-to-video diffusion models for tasks like domain transfer and 3D scene generation by conditioning on disentangled appearance features. Many applications in generation and transfer rely on conditioning these models, typically through perceptual, geometric, or simple...
METHOD
Video models have recently been applied with success to problems in content generation, novel view synthesis, and, more broadly, world simulation. Many applications in generation and transfer rely on conditioning these models, typically through perceptual, geometric, or simple s...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this paper, we show how we can use the features for tasks such as video domain transfer and video-from-3D generation. Code availability is flagged in the production record; the public repository link s...
WHY NOW
Generative Video moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
We introduce a lightweight architecture and training strategy that decouples appearance from other features that we wish to preserve, enabling robust control for appearance changes such as stylization and relighting.
Directly stated in the abstract with specific technical details about the method's purpose and capabilities.
partial
In this paper, we show how we can use the features for tasks such as video domain transfer and video-from-3D generation.
Directly stated in the abstract as a demonstrated capability of the method.
partial
Features obtained through self-supervised learning like DINO, contain a lot of entangled information about style, lighting and semantics of the scene.
Directly stated in the abstract as a factual description of existing feature properties.
partial
This makes them great at reconstruction tasks but limits their generative capabilities.
Directly stated in the abstract as a limitation of existing features that the paper addresses.
partial
Furthermore, we show that low spatial resolution can be compensated by higher feature dimensionality, improving controllability in generative rendering from explicit spatial representations.
Directly stated in the abstract as a technical finding, though slightly more general than other claims.
partial
Control-DINO: Feature Space Conditioning for Controllable Image-to-Video Diffusion
Directly stated in the title and implied throughout the abstract as the core contribution.
partial
At the same time, high-dimensional features obtained from large-scale self-supervised learning on images or point clouds are increasingly used as a general-purpose interface for vision models.
Directly stated in the abstract as background context, representing a current trend in the field.
partial
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Control image-to-video diffusion models for tasks like domain transfer and 3D scene generation by conditioning on disentangled appearance features.
Segment
Generative Video
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