Sketch2Colab: Sketch-Conditioned Multi-Human Animation via Controllable Flow Distillation
This addresses the challenge of creating physically plausible, storyboard-faithful multi-human animations for applications like film, gaming, or simulation, though it appears incremental as it builds on existing diffusion and flow-based methods.
The paper tackles the problem of generating coherent 3D multi-human motion from 2D sketches with precise control over interactions, achieving state-of-the-art constraint adherence and perceptual quality while offering significantly faster inference than diffusion-only baselines.
We present Sketch2Colab, which turns storyboard-style 2D sketches into coherent, object-aware 3D multi-human motion with fine-grained control over agents, joints, timing, and contacts. Conventional diffusion-based motion generators have advanced realism; however, achieving precise adherence to rich interaction constraints typically demands extensive training and/or costly posterior guidance, and performance can degrade under strong multi-entity conditioning. Sketch2Colab instead first learns a sketch-driven diffusion prior and then distills it into an efficient rectified-flow student operating in latent space for fast, stable sampling. Differentiable energies over keyframes, trajectories, and physics-based constraints directly shape the student's transport field, steering samples toward motions that faithfully satisfy the storyboard while remaining physically plausible. To capture coordinated interaction, we augment the continuous flow with a continuous-time Markov chain (CTMC) planner that schedules discrete events such as touches, grasps, and handoffs, modulating the dynamics to produce crisp, well-phased human-object-human collaborations. Experiments on CORE4D and InterHuman show that Sketch2Colab achieves state-of-the-art constraint adherence and perceptual quality while offering significantly faster inference than diffusion-only baselines.