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An image-to-video pipeline needs an intermediate representation when the generated clip must follow controls that the input image cannot supply by itself: which object moves and how, where the camera travels, or what the scene’s geometry is over time. The representation sits between the still image and the output frames and gives later stages an explicit, inspectable instruction to work from, instead of leaving those controls implicit in a prompt or buried inside the model.
What the representation does
An intermediate representation makes information available to later stages in a form they can actually use. In image-to-video work, that information usually takes one of three shapes: an object-aware motion instruction, an explicit camera path, or a scene model that holds geometry and motion together.
The cited work points to two broad design families. The first guides motion directly in image space. It encodes what should move and in which regions, which keeps the representation compact and direct to produce. The second builds an explicit 3D scene model. That model exposes geometry, can be viewed from camera positions the input image never showed, and makes camera-aware rendering possible, but it adds modeling choices and new consistency requirements. These distinctions are design implications drawn from how the methods are built, not a measured ranking of one family over the other.
Representations in current work
The six papers below cover the main options. Each is described from its own published abstract or project page.
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Mask-based motion trajectories (Through-The-Mask)
Meta AI’s project page for Through-The-Mask, which concerns mask-based motion trajectories for image-to-video generation, states the central idea directly: “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 the page, not a quotation from a named author. The practical point is that motion is tied to object regions, so the instruction says which part of the image moves and how, in one compact object.
Persistent 3D scene with motion bases (Shape of Motion)
Shape of Motion, presented at ICCV 2025 under the title “4D Reconstruction from a Single Video,” represents a dynamic scene with canonical 3D Gaussians, SE(3) motion bases, and per-Gaussian motion coefficients. Its described inputs are RGB frames, monocular depth, and 2D tracks, and it compares rendered RGB, depth, and tracks against the corresponding input signals. This is a reconstruction method rather than a generator, but it shows the central pattern clearly: appearance and geometry persist as one canonical object, while motion is expressed as time-varying parameters on top of it.
Separate camera and object controls (SymphoMotion)
SymphoMotion, presented at CVPR 2026 as joint control of camera motion and object dynamics, uses explicit camera paths and geometry-aware cues for camera control, and 3D trajectory embeddings for object dynamics. Its framing rests on one principle: camera-induced parallax and genuine object movement are different signals and can be controlled separately. Section details on that separation follow below.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsDeformable Gaussian field from video diffusion (Diff4Splat)
Diff4Splat, presented at CVPR 2026 under the title “Repurposing Video Diffusion Models for Dynamic Scene Generation,” generates a deformable 3D Gaussian field from an image, a camera trajectory, and an optional text prompt, using a video latent transformer. Its abstract names three design objectives: appearance fidelity, geometric accuracy, and motion consistency. Those are objectives the paper sets for its method, not guarantees that every system built on the idea will meet them.
Hierarchical Gaussian video representation (GaussianVideo)
GaussianVideo, presented at ICCV 2025 as efficient video representation via hierarchical Gaussian splatting, combines 3D Gaussian splatting with continuous camera-motion modeling and hierarchical spatiotemporal refinement. It is aimed at dynamic video reconstruction rather than image-to-video generation, which makes it useful as a reference for how a Gaussian-based representation can be refined across space and time.
Coarse geometry guiding diffusion (VideoFrom3D)
VideoFrom3D, presented at SIGGRAPH Asia 2025 under the title “3D Scene Video Generation via Complementary Image and Video Diffusion Models,” takes coarse geometry, a camera trajectory, and a reference image as inputs. Geometry- and camera-derived cues then guide both an image-diffusion stage and a video-diffusion stage. This is the clearest example of an explicit structure conditioning generative synthesis, rather than being rendered directly into the output.
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How the approaches compare
No head-to-head benchmark across these six papers was found, so the comparison below uses only the differences their descriptions state. A cell reads “not stated” where the cited description does not give that property.
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|---|---|---|---|---|---|
| Through-The-Mask (Meta AI project page; date not stated) | Object semantics and motion in a mask-based trajectory | Not stated | Object motion via mask trajectory; camera control not stated | Not stated | Not stated |
| Shape of Motion (ICCV 2025) | Canonical 3D Gaussians, SE(3) motion bases, per-Gaussian coefficients | Yes: persistent 3D Gaussians | Scene motion from a single video; separate camera control not stated | Shared motion bases across scene elements | Not stated |
| SymphoMotion (CVPR 2026) | Explicit camera paths, geometry-aware cues, 3D trajectory embeddings | Geometry-aware cues for camera control | Camera and object dynamics controlled separately | Not stated | Not stated |
| Diff4Splat (CVPR 2026) | Deformable 3D Gaussian field generated from image, camera trajectory, optional text | Yes: 3D Gaussian field | Camera trajectory is an input; object motion via the deformable field | Motion consistency is a stated design objective | Not stated |
| GaussianVideo (ICCV 2025) | 3D Gaussian splatting with continuous camera-motion modeling | Yes: 3D Gaussian splatting | Continuous camera-motion modeling; object control not stated | Hierarchical spatiotemporal refinement | Not stated |
| VideoFrom3D (SIGGRAPH Asia 2025) | Coarse geometry, camera trajectory, reference image | Yes: coarse geometry | Camera trajectory is an input; object control not stated | Not stated | Not stated |
The comparison axes that matter most are what the representation encodes, whether it is image-space or an explicit 3D scene state, whether camera and object motion can be specified independently, and how motion is kept coherent over time. Pipeline cost is listed for completeness, but none of the cited descriptions reports a figure for it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Separating camera motion from object motion
Camera and object movement look similar in a finished clip, which is why they are easy to confuse in a pipeline. Parallax appears when the viewpoint moves past a still object, and it also appears when the object itself travels. A single entangled control cannot tell those apart.
Consider a product shot built from one photograph of a bottle on a table. One version orbits the camera around a motionless bottle. Another keeps the camera fixed while the bottle rolls. Both clips show the bottle shifting against the background, but they require different instructions. A representation that keeps a camera path and an object trajectory as separate channels lets the same image produce either clip, or a combination, without rewriting the whole prompt. This is the principle SymphoMotion builds on.
Deciding whether your pipeline needs one
Use the following checks to decide whether an intermediate representation earns its place in your pipeline.
Quick Recap
- You need the same motion reused across generations. An explicit trajectory, mask, or scene state can be saved, edited, and applied again. A prompt-only approach is harder to reproduce.
- You need camera and object motion specified independently. Choose a representation with separate camera and object channels, such as the joint-control approach in SymphoMotion.
- You need views the input image does not show. Choose an explicit 3D representation, such as the Gaussian-based or coarse-geometry approaches above, because image-space guidance cannot supply unseen geometry.
- You need only a loose motion hint. A full scene representation is probably more machinery than the task requires. Image-space guidance is the simpler starting point.
- You are optimizing for fewer stages. Count the stages each option adds. The cited descriptions do not quantify this, so measure it on your own pipeline before committing.
Limits of the evidence
- The reviewed sources do not establish a universal representation standard, and none identifies a single best representation for every pipeline. The choice depends on the control, consistency, and complexity your pipeline needs.
- No common benchmark across these six papers was found. The differences described here come from each paper’s own description of its method.
- Each paper reports its own evaluation. This article quotes no numeric results, and none should be read as a cross-paper comparison.
- Diff4Splat’s abstract names appearance fidelity, geometric accuracy, and motion consistency as design objectives. Those are targets the authors set, not outcomes guaranteed for every system.
- Temporal consistency and pipeline cost are described qualitatively at most. Treat claims about either as design considerations until you test them in your own setup.
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