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An image-to-video pipeline needs an intermediate representation when you must control something the input image does not carry explicitly: which objects move and how, where the camera goes, or what the underlying 3D scene looks like. Without that layer, the generator has to infer motion, viewpoint, and geometry from pixels alone, and you have little reusable handle on any of them. The representation is the boundary where those controls become explicit. The cited work shows several different ways to build that boundary, and none of it establishes a single standard format that suits every pipeline.
What an intermediate representation does in image-to-video
An intermediate representation sits between the input image and the generated frames. Later stages read it instead of raw pixels, so the information it holds stays editable, inspectable, and reusable. For image-to-video, that information usually falls into one of three kinds: an object-aware motion instruction, an explicit camera path, or a scene model that holds geometry and motion over time.
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The practical question is not whether a representation is “better,” but what it has to carry. A pipeline that only needs a user to say “the dog moves left while the camera holds still” can work with a compact motion signal. A pipeline that must render the same scene from new viewpoints, or keep geometry stable across a long camera move, needs a representation that models 3D structure.
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Mask-based motion trajectories
Meta AI’s Through-The-Mask page proposes a mask-based motion trajectory as the intermediate layer for image-to-video generation. The page describes it as a single structure that carries object semantics and motion together. That pairing is the main appeal: the representation says which region is which object and how that region moves, without building a full 3D model. The trade-off is that the representation is image-space. It tells the generator where things move on the picture plane, not what the scene looks like from another angle.
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The page makes this point in its own words:
“Our key innovation is the introduction of a mask-based motion trajectory as an intermediate representation, that captures both semantic object information and motion, enabling an expressive but compact representation of motion and semantics.”
This is an institutional statement on Meta’s page, not a quotation from a named author.
Explicit 3D scene models
Shape of Motion (ICCV 2025) takes the opposite route. It represents a dynamic scene with persistent canonical 3D Gaussians, a small set of SE(3) motion bases, and per-Gaussian motion coefficients. Its described inputs are RGB frames, monocular depth, and 2D tracks, and it checks its model by comparing rendered RGB, depth, and tracks against the corresponding signals. The useful idea here is separation: appearance and geometry persist as a canonical scene, while motion is a time-varying layer applied to it.
Diff4Splat (CVPR 2026) pushes this idea into generation. It produces a deformable 3D Gaussian field from an image, a camera trajectory, and an optional text prompt, using a video latent transformer. The paper states its design objectives as appearance fidelity, geometric accuracy, and motion consistency. Those are objectives the authors set for their method, not guarantees for every system built on similar ideas.
VideoFrom3D (SIGGRAPH Asia 2025) uses a related but lighter structure. It takes coarse geometry, a camera trajectory, and a reference image, and it uses geometry- and camera-derived cues to guide both an image-diffusion stage and a video-diffusion stage. Here the explicit structure conditions the generator rather than being rendered directly.
Separate camera and object control
SymphoMotion (CVPR 2026) addresses a different problem: keeping two kinds of motion apart. It uses explicit camera paths with geometry-aware cues for camera control, and 3D trajectory embeddings for object dynamics. Its framing rests on the observation that camera-induced parallax and real object movement are different signals. If a single motion input mixes them, a user who wants a moving object in a still camera shot, or a moving camera past a static object, cannot easily specify one without disturbing the other.
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Gaussian representations for video
GaussianVideo (ICCV 2025) combines 3D Gaussian splatting with continuous camera-motion modeling and hierarchical spatiotemporal refinement. It is aimed at efficient video representation and dynamic reconstruction rather than prompt-driven generation. It is useful to know about because it shows the same Gaussian building block serving reconstruction, while the generative examples above use it as a target for diffusion models.
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The cited papers do not share a common benchmark, so the table below compares what each work describes, not how the approaches perform against one another. Where a source does not describe a property, the cell says so.
| Approach | Source and date | What it encodes | Explicit 3D structure | Camera and object control | Temporal consistency mechanism | Pipeline cost |
|---|---|---|---|---|---|---|
| Mask-based motion trajectory | Meta AI, Through-The-Mask page (publication date not stated) | Object semantics and motion in image space | No | Object motion via masks; camera control not stated | Not stated | Not stated |
| Canonical 3D Gaussians with motion bases | Shape of Motion, ICCV 2025 | Persistent appearance and geometry plus time-varying motion | Yes | Scene motion from a single video; generative camera control not stated | Shared SE(3) motion bases with per-Gaussian coefficients | Not stated |
| Camera path with 3D trajectory embeddings | SymphoMotion, CVPR 2026 | Camera path, geometry-aware cues, object trajectories | Geometry-aware cues; a full scene model is not stated | Separate camera and object controls | Not stated | Not stated |
| Deformable 3D Gaussian field from video diffusion | Diff4Splat, CVPR 2026 | Scene geometry and deformation from image and camera trajectory | Yes | Driven by camera trajectory; optional text prompt | Motion consistency is a stated design objective | Not stated |
| Coarse geometry guiding image and video diffusion | VideoFrom3D, SIGGRAPH Asia 2025 | Coarse geometry and camera trajectory from a reference image | Yes, coarse | Camera trajectory input | Not stated | Not stated |
| Hierarchical Gaussian video representation | GaussianVideo, ICCV 2025 | Video as 3D Gaussians with camera-motion modeling | Yes | Continuous camera-motion modeling | Hierarchical spatiotemporal refinement | Not stated |
When your pipeline needs one
An intermediate representation earns its place when at least one of these conditions holds:
- Users must edit motion after the image is chosen, and the edit must survive regeneration.
- Camera movement and object movement must be controlled independently, as the SymphoMotion framing suggests.
- The output must be rendered from viewpoints beyond what the input image shows, which points toward an explicit 3D model.
- You need to inspect or debug the motion signal before it reaches the generator, rather than only the final frames.
When a direct image-to-video model is enough
If the output only needs a plausible short clip from one image, with a text prompt or a single motion hint, a direct model without an explicit layer avoids an extra modeling stage. The same applies when the scene is simple and no camera path must be honored. The cited work does not show that intermediate representations always improve results, so adding one should be justified by a control you actually need.
Trade-offs to plan for
- Explicitness costs modeling effort. A 3D scene representation requires decisions about geometry, canonical structure, and how motion is parameterized. Mask trajectories are simpler to author but say less about depth.
- Consistency is a design choice, not a free property. Diff4Splat names motion consistency as an objective; the other works describe their own mechanisms. Check consistency on your own content rather than assuming it carries over.
- Input requirements vary. Shape of Motion relies on monocular depth and 2D tracks. Pipelines built on similar inputs inherit their errors.
- There is no shared format. If you choose a representation now, keep its interface behind your own abstraction so you can replace it as methods change.
Start by writing down which of the controls above your product must expose. The representation that carries exactly those controls, and no more, is the one your pipeline needs.
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