cs.CVSep 4, 2026
SaveUniMate: One Unified Model to Animate Diverse Skeletons
Organizations: Princeton University, USA · University of California, Berkeley, USA · Massachusetts Institute of Technology, USA · Nanyang Technological University, Singapore
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 introduces 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. Our project page is available at https://linzhanmou.com/unimate/.
Figures & tables
Figure 1. Given a rigged 3D asset and a text prompt, UniMate generates animations for characters with diverse skeletal topologies within a single unified model. Examples of diverse rigged 3D characters and skeletons animated by UniMate from text prompts.
Figure 2. Animations generated by UniMate. Our method generalizes across heterogeneous skeletons and diverse motion prompts. Qualitative UniMate animation sequences for characters with heterogeneous skeletons and motion prompts.
Figure 3. UniMate pipeline. The proposed TADiT operates on a joint token space that combines per-frame motion features with rest-pose skeletal descriptors, while injecting skeletal graph-structured bias into both positional encoding and attention computation. Conditioned on input text prompts, our unified model produces realistic, coherent animations while demonstrating strong cross-topology generalization and efficient runtime performance. UniMate pipeline from a rigged mesh and text prompt through topology-aware joint tokens to generated skeletal animation.
Figure 4. Cross-topology animation. UniMate generates prompt-aligned motions for diverse characters and articulated objects within a single unified model. Animation sequences generated for quadrupedal, bipedal, plant-like, avian, and articulated-object skeletons from text prompts.
Figure 5. One skeleton, diverse prompts. Given a single skeleton, UniMate synthesizes distinct, prompt-faithful motions for different input text prompts. One character skeleton animated according to several different text prompts. Several distinct motion samples generated for the same character skeleton and text prompt.
Figure 7. Spectral visualization. From left to right, Laplacian eigenvectors increase in frequency. Low-frequency modes capture global kinematic structure, while higher-frequency modes encode finer local relationships. Skeleton joints colored by graph Laplacian eigenvectors from low to high spatial frequency.
Figure 8. Long-horizon generation. A variant trained at 180 frames generates long sequences that stay temporally coherent and drift-free. Three rows of eleven keyframes, each from a single generated 180-frame sequence: a desk lamp scanning around, hopping forward, and settling low; a robot duck walking forward, performing a forward roll, turning right, and sitting down; and a robot arm picking up an object, carrying it, and placing it.
Figure 9. Samples from the UniML3D dataset. Our dataset spans diverse skeletons across bipedal, quadrupedal, avian, marine, insectoid, serpentine, and articulated rigid objects, with detailed skeleton annotations and coherent text prompts paired with motion sequences. Representative UniML3D assets, skeletons, and motions across multiple morphology categories.
Figure 10. UniML3D data-processing pipeline. Source assets pass through skeleton-based filtering, semantic annotation, and motion canonicalization; balanced sampling and augmentation happen on the fly during training. Flowchart of the UniML3D curation pipeline: three motion sources feed skeleton-based filtering, semantic annotation, and motion canonicalization, producing the dataset, which is consumed at training time with balanced sampling and augmentation.
Figure 11. Comparison with AnyTop on unseen Truebones skeletons. Ours (left) vs. AnyTop (right): our deer falls and lies on its side and our raptor walks forward as prompted, whereas AnyTop’s deer never falls and its raptor remains nearly stationary. Side-by-side motion sequences comparing UniMate and AnyTop on unseen animal skeletons. Two groups of animation sequences, one per text prompt. Each group shows four characters with different skeletal topologies—a bird, a many-legged insect, a stegosaurus, and a leopard—each animated over four frames with its skeleton overlaid on the mesh.
Figure 13. Motion in-betweening. Given the start and end poses and a text prompt, UniMate synthesizes smooth and plausible intermediate motions; the opaque poses mark the given boundary constraints, while the translucent poses are the synthesized in-betweens. Motion in-betweening sequences showing fixed endpoint poses and synthesized intermediate poses. Text-guided motion editing examples with selected joints fixed and the resampled joints outlined by dashed boxes. A motion-expansion sequence in which a character stands up, walks forward, and then turns around in place under three consecutive text prompts.
| Method | FID | Diversity |
|---|---|---|
| w/o graph-aware attention bias | 0.773 | 8.799 |
| w/o Spec-RoPE | 0.798 | 8.424 |
| w/o global topological conditioner | 0.825 | 9.475 |
| Full Model | 0.757 | 9.200 |
Table 4. Ablation. Every topology-aware component improves quality. Ablation results showing motion quality and diversity after removing each topology-aware component.
Figure 16. Foot sliding. In a generated walk, the hind-paw contacts drift along the ground (red traces) instead of staying planted. Four overlaid instants of a generated leopard walk; red trajectory traces at the hind paws show the contact points sliding along the ground instead of staying planted.
Figure 17. Rare-topology failure. Prompted to fall onto the desk, the lamp only dips and recovers its head; the base never tips over. Six frames of a desk lamp prompted to fall forward onto the desk; the lamp head dips and recovers while the base never leaves the desk.
Appendix figures & tables10 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 18. Morphology distribution of UniML3D. Share of motion sequences per skeletal morphology; bipedal characters dominate, while serpentine and marine rigs are rare. Pie chart of the share of UniML3D motion sequences per skeletal morphology category, dominated by bipedal characters.
Figure 19. Joint-count distribution across skeletons. Histogram and kernel density of the number of joints per retained skeleton in UniML3D (median 38, mean 40.4); the bimodal spread shows that the dataset covers rigs of widely varying complexity. Histogram of joint counts across skeletons retained in UniML3D.
| Skeleton | Prompt | Skeleton | Prompt |
|---|---|---|---|
| Crab | A crab attacks forward. | Leapord | A leopard attacks forward. |
| A crab lunges forward to attack. | A leopard crouches low and bites forward. | ||
| A crab strikes forward with its claws. | A leopard collapses and lies on its side. | ||
| A crab snaps its claws and attacks. | A leopard falls backward and lies on its side. | ||
| A crab lowers its head and lies down. | A leopard crouches low. | ||
| A crab collapses backward and lies down. | A leopard growls with its head lowered. |
Table 5. The held-out evaluation set. The 58 evaluation prompts of the seven held-out Truebones skeletons (Crab 10, Jaws 8, KingCobra 8, Parrot2 2, Leapord 12, Raindeer 8, Raptor 10), grouped by skeleton type; each prompt is the exact text used to condition generation.
Figure 20. User-study interface and protocol. (a) Instruction page presented at the start of each session, with institution-identifying information masked. (b) A representative trial: three side-by-side videos generated by different methods from the same input mesh and text prompt, followed by the four evaluation questions used for subjective scoring; method–label assignments are anonymized and randomized per participant.
Figure 21. User study results on text-conditioned mesh animation. Mean Likert ratings (1 = very poor, 5 = excellent, with error bars) per criterion and averaged overall; UniMate receives the highest rating on all four criteria—text-to-motion agreement, motion plausibility, motion expressiveness, and shape preservation—as well as overall. Grouped bar chart of mean Likert ratings for V2M4, AnimateAnyMesh, and UniMate on four criteria and their overall average; UniMate scores highest on every axis.
Figure 22. Additional comparisons with AnyTop on unseen Truebones skeletons. Rest-pose asset and prompt (left), our result (middle), and AnyTop (right), extending the main-paper comparison to an avian glide and a crustacean collapse. Our parrot flaps and glides forward and our crab collapses backward onto its shell as prompted, whereas AnyTop’s parrot hovers flapping in place and its crab keeps stepping with raised claws without collapsing.
Figure 23. Qualitative comparison with skeleton-free baselines. Four evenly spaced frames per clip on two user-study cases, cropped around the subject for visibility. UniMate executes the prompted action with dynamic, coherent motion—the lion’s leap-and-bite and the anaconda’s upward coil; AnimateAnyMesh remains near-static on both cases, while V2M4 distorts the lion’s mane geometry and raises only the anaconda’s head, never producing the prompted coil. Two blocks of frame sequences comparing UniMate, AnimateAnyMesh, and V2M4 on a leaping lion and an anaconda coiling upward.
Figure 24. Qualitative ablation of the topology-aware components. The same rig and prompt rendered from the already-trained ablation models of the main paper, with each variant’s FID and diversity inset. The full model executes a clear forward attack; without the graph-aware attention bias the wing–body coordination degrades; without Spec-RoPE the motion loses the prompted action and collapses toward in-place wing flailing; without the global topological conditioner the motion stays dynamic but becomes unstable, with jittery, poorly grounded poses. Four rows of flamingo motion frames comparing the full model against three ablated variants, each row annotated with its FID and diversity.
Figure 25. Before/after qualitative examples for the curation stages. The same held-out rig and prompt rendered from models trained with the full curation pipeline (top) and with one stage removed. Without implausibility filtering, the implausible clips that survive in the corpus surface at generation time: the reindeer tumbles through airborne, twisted poses instead of attacking. Without dataset-level statistics normalization, the attack is roughly executed but the motion drifts and hovers with degraded ground contact. The full pipeline performs the prompted antler attack with stable, grounded motion. Three rows of reindeer motion frames for the same attack prompt: the full curated pipeline produces a grounded antler attack, the variant without implausibility filtering tumbles into airborne, twisted poses, and the variant without dataset-level statistics normalization drifts and hovers with poor ground contact.
| Method | Skate | Slide |
|---|---|---|
| UniMate (Ours) | 0.107 | 0.542 |
| UniMate + FL | 0.023 | 0.191 |
Table 6. Foot-sliding evaluation on held-out legged skeletons. Skate is the skating ratio and Slide the height-weighted sliding distance ( Section E.5 ), the latter in units of for a rest-pose root height . FL denotes the foot-locking post-process.