Given a rigged 3D asset and a text prompt, UniMate generates animations for
characters with diverse skeletal topologies within a single unified model.
Interactive Examples.
Explore our interactive 3D animation examples. Click on any example in the
sidebar to view it. Toggle the skeleton and playback from the controls
panel; drag to rotate, scroll to zoom.
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Abstract.
Recent advances in automatic rigging now deliver animation-ready 3D assets
at scale, yet generating the motion to drive them remains a bottleneck.
Existing learned animators are topology-constrained: they rely on
category-specific templates or require per-skeleton fine-tuning and reference
motions at inference. We present UniMate, a unified foundation model
that synthesizes articulated motion for arbitrary skeletons from a rigged 3D
asset and a text prompt, with no test-time optimization or per-skeleton
retraining. UniMate relies on a topology-aware diffusion transformer, which
integrates skeletal topology into attention via three mechanisms: (1) a
graph-aware attention bias from pairwise joint relations and geodesic
distances; (2) a spectral rotary position embedding generalizing RoPE to
arbitrary kinematic trees via the graph Laplacian; and (3) a global
topological conditioner attention-pooled from the rest-pose skeleton. We also
curate UniML3D, 13,006 motion sequences spanning bipedal, quadrupedal, avian,
marine, insectoid, serpentine, and articulated rigid objects with unified
canonicalization and text pairing. Trained on this dataset, UniMate
outperforms state-of-the-art baselines in quality, generalization, and
efficiency, and supports zero-shot cross-topology transfer, in-betweening,
expansion, and text-guided editing.
Experiments.
1. Diverse Skeletons, Diverse Prompts.
Diverse skeletons, diverse prompts. One model, no per-skeleton
retraining: animals, plants, humanoids, robots, and everyday rigged objects,
each animated from the prompt shown above its panel. The three studies below
hold one factor fixed at a time.
2. One Prompt, Diverse Skeletons.
One prompt, diverse skeletons. Given a single text prompt, UniMate
generates plausible motions across heterogeneous skeletons.
3. One Skeleton, Diverse Prompts.
One skeleton, diverse prompts. Given a single skeleton, UniMate
synthesizes diverse motions that follow different input text prompts.
4. One Skeleton, One Prompt, Diverse Motions.
One skeleton, one prompt, diverse motions. Given the same skeleton and
text prompt, UniMate generates diverse plausible motion samples.
Applications.
1. Motion Editing.
Motion editing. Given a source motion (left), we keep a subset of joints
fixed and resample the remaining joint trajectories under a new prompt (right).
2. Motion In-Betweening.
Motion in-betweening. Given the start and end poses and a text prompt,
UniMate synthesizes smooth and plausible intermediate motions.
3. Motion Expansion.
Motion expansion. UniMate chains a sequence of text prompts to extend a
motion into a longer, temporally coherent sequence.
BibTeX
@inproceedings{mou2026unimate,
title = {UniMate: One Unified Model to Animate Diverse Skeletons},
author = {Mou, Linzhan and Lei, Jiahui and Dou, Zhiyang and Cai, Chenyue and
Song, Chaoyue and Finkelstein, Adam and Rusinkiewicz, Szymon},
booktitle = {SIGGRAPH Asia 2026},
year = {2026}
}
