[2603.02190] Sketch2Colab: Sketch-Conditioned Multi-Human Animation via Controllable Flow Distillation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.02190 (cs) [Submitted on 2 Mar 2026] Title:Sketch2Colab: Sketch-Conditioned Multi-Human Animation via Controllable Flow Distillation Authors:Divyanshu Daiya, Aniket Bera View a PDF of the paper titled Sketch2Colab: Sketch-Conditioned Multi-Human Animation via Controllable Flow Distillation, by Divyanshu Daiya and 1 other authors View PDF HTML (experimental) Abstract: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, a...