"MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation"
Below podcast on this paper is generated with Google's Illuminate.
https://arxiv.org/abs/2502.04299
The paper addresses the problem of limited user control in image-to-video generation, particularly for cinematic shot design involving both camera and object motions. Current methods lack intuitive and precise control over these intertwined movements, hindering creative expression.
This paper proposes MotionCanvas, a system that enables cinematic shot design by integrating user-driven controls for camera and object motions into image-to-video generation models. It uses a Motion Signal Translation module to bridge user intent and model conditioning.
-----
📌 MotionCanvas introduces a crucial Motion Signal Translation module. This module smartly converts user-friendly 3D motion intents into 2D screen-space signals. This bridging is key for effective video diffusion model control.
📌 The strength of MotionCanvas lies in achieving 3D-aware cinematic control using only 2D training data. This bypasses the need for costly 3D annotations. It leverages depth estimation and geometric transformations for scene understanding.
📌 MotionCanvas offers immediate practical value. It empowers users to design complex cinematic shots with combined camera and object motions. This enhanced control improves creative workflows in video content creation tasks.
----------
Methods Explored in this Paper 🔧:
→ MotionCanvas uses a Motion Design Module to capture user intentions for camera motion, object global motion, and object local motion. Users can define camera paths with 3D poses or base motion patterns. Scene-anchored bounding boxes control object global motion. Point trajectories control object local motion. Timing control is also integrated.
→ The Motion Signal Translation module converts scene-space motion designs into screen-space motion signals. Camera movement is represented by 2D point tracks derived from 3D camera paths using depth estimation. Scene-aware object motion uses bounding box trajectories converted to screen space, considering camera motion. Object local motion uses point trajectories transformed to screen space, accounting for both camera and global object motion.
→ A motion-conditioned video generation model, based on Diffusion Transformer (DiT), is fine-tuned with these screen-space motion signals. Point trajectories are encoded using Discrete Cosine Transform (DCT) coefficients. Bounding box sequences are encoded as color-coded masks. Auto-regressive generation is used for variable-length videos.
-----
Key Insights 💡:
→ Cinematic shot design requires simultaneous and intuitive control over camera and object motions in image-to-video generation.
→ MotionCanvas effectively translates user-defined scene-space motion intent into screen-space control signals for video diffusion models.
→ Representing camera motion with point tracking and object motion with scene-anchored bounding boxes allows for 3D-aware control without explicit 3D training data.
→ DCT coefficients and color-coded masks provide efficient and effective ways to condition video diffusion models on motion signals.
-----
Results 📊:
→ MotionCanvas achieves lower Rotation Error (0.6334) and Translation Error (0.2188) in camera motion control compared to MotionCtrl and CameraCtrl.
→ MotionCanvas reduces Frechet Video Distance (FVD) to 34.09 and Frechet Inception Distance (FID) to 7.60, indicating higher video quality.
→ User studies show MotionCanvas is preferred for motion adherence (75.24%), motion quality (79.05%), and frame fidelity (77.14%) over DragAnything and MOFA-Video.