Text-guided dynamic 3D character generation has advanced rapidly, yet producing high-quality motion that faithfully reflects rich textual descriptions remains challenging. Existing methods tend to generate limited sub-actions or incoherent motion due to fixed-length temporal inputs and discrete frame-wise representations that fail to capture rich motion semantics. We address these limitations by representing motion with continuous differentiable B-spline curves, enabling more effective motion generation without modifying the capabilities of the underlying generative model. Specifically, our closed-form, Laplacian-regularized B-spline solver efficiently compresses variable-length motion sequences into compact representations with a fixed number of control points. Further, we introduce a normal-fusion strategy for input shape adherence along with correspondence-aware and local-rigidity losses for motion-restoration quality. To train our model, we collate BIMO, a new dataset containing diverse variable-length 3D motion sequences with rich, high-quality text annotations. Extensive evaluations show that our feed-forward framework BiMotion generates more expressive, higher-quality, and better prompt-aligned motions than existing state-of-the-art methods, while also achieving faster generation. Our project page is at: https://wangmiaowei.github.io/BiMotion.github.io/.
Figures & tables
Figure 2: Overview. BiMotion uses a B-spline representation for motion generation. During training (red arrow), vertex differences are converted into control points and encoded into motion latents. During inference (black arrow), the initial mesh and the text prompt generate motion latents that are decoded into control points and converted into the generated mesh sequence via B-spline reprojection.
Figure 3: B-spline VAE Pipeline. Given the initial shape (Pn,0,N0) and control points Pn,k , the Encoder compresses them into latent codes z0 and zk . The Decoder reconstructs the predicted control points P^n,k , which are then reprojected to point differences via the B-spline basis. Note, * indicates that Fk′ uses the FPS-sampled indices of F0′ , ⊕ denotes matrix addition, and ⊗ denotes matrix multiplication.
Figure 4: Qualitative Comparisons. Our method (BiMotion) results in superior motion quality and is more aligned with the user-provided text prompts. Artifacts for the baseline methods are highlighted in red. Please see the supplementary material for additional results.
Methods
Vbench
OC ↑
SC ↑
TF ↑
AQ ↑
DD↑
GVFDiffusion [ 116 ]
0.167
0.920
0.986
0.505
0.650
AnimateAnyMesh [ 101 ]
0.155
0.951
0.993
0.514
0.100
V2M4 [ 12 ]
0.175
0.876
0.986
0.478
0.750
BiMotion (Ours)
0.187
0.948
0.995
0.529
0.800
Methods
User Study
Efficiency
Table 1: Quantitative Comparisons. Our approach not only surpasses existing methods across multiple metrics ( Sec. 4.3 ), but also requires less time and peak GPU memory (PG) on an A100 GPU.
B-Spline
NF
Control-PE
LCorr
LRigid
Rec Error ↓
✗
✗
✗
✗
✗
3.237
✗
✓
✗
✓
✓
2.674
✓
✗
✓
✓
✓
1.328
✓
✓
✗
✓
✓
1.648
✓
✓
✓
✗
✓
1.303
✓
✓
✓
✓
✗
1.349
Table 2: VAE Quantitative Ablations. “Rec Error” indicates the mean L1 error ( ×10−2 ) averaged per trajectory and per instance.
Figure 5: B-spline Ablation. B-spline interpolation from predicted control points achieves lower L1 error over the entire sequence than linear interpolation on sampled raw differences.
Figure 6: Laplacian Regularizer L . B-spline ×2 interpolation comparison from a T=10 mesh sequence comparing Ridge [ 36 ] and our Laplacian regularizer L . L produces more natural results.
Figure 7: Normal Fusion (NF) Ablation. NF effectively separates nearby distinct parts. Without it, artifacts appear (see red).
Figure 8: Control-PE Ablation. Our control-PE (Bottom) captures fine motion ( e.g., the lion’s tail) and reduces artifacts (red circles) compared to conventional position encoding (Middle).
Figure 9: LRigid Ablation. Our local rigid loss reduces the red-circled artifacts during motion generation. Prompt: “The pistol tilts up to an angled pose, then returns to forward-facing.”
Figure 10: Robustness to Meshing. Ours produces consistent motion under mesh changes (Top to Bottom), whereas AnimateAnyMesh exhibits unstable motion and artifacts (red circles).
Appendix figures & tables15 assets
Supplementary material from the paper’s appendix.
Appendix
Figure A1 : Velocity Architecture. The network vθvel comprises S′ stacked layers with self- and cross-attention modules.
Figure A3 : Additional Qualitative Comparisons with AnimateAnyMesh. (a) AnimateAnyMesh [ 101 ] and (b) BiMotion (Ours).
Figure A4 : Puppeteer Qualitative Results. Results are rendered using the authors’ open-source renderer, whose texture appearance differs from ours and AnimateAnyMesh.
Figure A5 : Video Generation Model Comparison. Qualitative comparison of video outputs from (a) Adobe Firefly [ 1 ] , (b) HunyuanVideo-I2V [ 47 ] , (c) Wan-2.5 [ 92 ] , and (d) Kling 2.1 [ 46 ] . Note: these generation models convert transparent backgrounds to black by default.
Number of control points
4
8
16
32
L2 Error ( ×10−3 ) ↓
68.7
22.6
6.85
1.81
Appendix
Table A1 : Ablation of the number of control points.
Figure A6 : VAE Reconstruction Error vs. Sequence Length. Mean L1 reconstruction error for our VAE, computed per trajectory and averaged per instance, reported for each sequence-length bin.
Figure A7 : INR methods like DNF can introduce shape artifacts.
w/o N0
N0 Frequency-PE
N0 Concat.
Ours
1.328
1.331
1.294
1.078
Appendix
Table A2 : Additional normal-fusion ablations. Mean L1 reconstruction error ( ×10−2 ), averaged per trajectory and per instance.
Figure A8 : User Study Interface and Protocol. Left: Instructions page. Screenshot of the questionnaire introduction; personal or institutional information approved by the research ethics committee has been masked. Middle: Video comparison interface showing the conditions of both the first frame and the textual prompt, along with four videos generated by four randomly ordered methods. Right: Three evaluation questions used to collect user preferences, forming the basis of the subjective evaluation metrics.
Methods
TA ↑
MP ↑
ME ↑
GVFDiffusion [ 116 ]
2.343±0.407
2.300±0.380
2.443±0.387
AnimateAnyMesh [ 101 ]
2.314±0.394
2.686±0.464
2.443±0.357
V2M4 [ 12 ]
2.876±0.368
2.714±0.354
3.048±0.348
BiMotion (Ours)
4.095±0.334
4.062±0.328
4.048±0.339
Appendix
Table A3 : User study results. Each cell shows the mean ± standard deviation over all participants and examples.
Figure A9 : Auto-captioning pipeline. We propose an iterative captioning pipeline that incorporates both a caption inspector and a caption generator to produce accurate and consistent captions for dynamic assets.
Figure A10 : Frame-length cumulative distribution. Distribution of mesh sequence lengths (length ≤200 ) from DeformingThings4D [ 52 ] , ObjaverseV1 [ 23 ] , and ObjaverseXL [ 22 ] .
Figure A11 : VLM Captioning Ablation. We compare captions generated by three video–language models (VLMs). Using GPT-5 produces concise and motion-faithful descriptions closely aligned with the real 4D dynamics.
We introduce T2Mo, a feed-forward framework for controllable dynamic 3D shape generation conditioned on 3D trajectories and text. Due to the inherent ambiguity of language, generating precisely intended motions using text alone remains challenging. To address this, we adopt 3D trajectories as controllable spatial guidance, specifying the exact paths along which selected points should move. By combining both, T2Mo generates object motions that spatially adhere to the given trajectories while globally reflecting the text semantics. To robustly handle trajectory inputs with arbitrary configurations, ranging from dense to sparse and unevenly distributed, we further propose a shape-grounded trajectory embedding that maps an input trajectory set into a shape-aware token set covering the entire object. We conduct extensive comparisons against text-based baselines and cascaded video-based baselines that combine trajectory-guided video generation with video-to-dynamic mesh generation. Quantitative and qualitative evaluations, along with user studies, demonstrate that our approach produces motions that more faithfully follow the given prompts with higher expressiveness while preserving motion quality.
Text-to-motion generation is driven by learning motion representations for semantic alignment with language. Existing methods rely on either continuous or discrete motion representations. However, continuous representations entangle semantics with dynamics, while discrete representations lose fine-grained motion details. In this context, we propose FlowCoMotion, a novel motion generation framework that unifies both treatments from a modeling perspective. Specifically, FlowCoMotion employs token-latent coupling to capture both semantic content and high-fidelity motion details. In the latent branch, we apply multi-view distillation to regularize the continuous latent space, while in the token branch we use discrete temporal resolution quantization to extract high-level semantic cues. The motion latent is then obtained by combining the representations from the two branches through a token-latent coupling network. Subsequently, a velocity field is predicted based on the textual conditions. An ODE solver integrates this velocity field from a simple prior, thereby guiding the sample to the potential state of the target motion. Extensive experiments show that FlowCoMotion achieves competitive performance on text-to-motion benchmarks, including HumanML3D and SnapMoGen.
Dawei Guan, Di Yang, Chengjie Jin +1
School of Artificial Intelligence and Data Science, University of Science and Technology of China · Suzhou Institute for Advanced Research, University of Science and Technology of China
Text-driven 3D human motion generation models face significant challenges in responding to diverse and unconstrained textual prompts, primarily due to the limited availability of 3D motion training data. To address this, we introduce MoVT, a novel framework that effectively leverages the extensive range of human action videos to enhance text-to-motion generation. At the core of our approach is the cross-modal augmented motion tokenizer, which projects discrete 3D motion tokens into the 2D domain. This projection allows us to enrich the motion codebook with complex, real-world motion patterns derived from videos. The enriched discrete tokens are then mapped back to the 3D domain, resulting in aligned 3D and 2D codebooks with an enhanced capacity to represent intricate motions. These enhanced codebooks are integrated into a generative masked transformer, which predicts masked motion token indices in a modality-agnostic manner. This enables the use of text-index pairs, generated from the 2D codebook and annotated motion videos, to further enhance the generator. Extensive empirical evaluations show that MoVT performs favorably against prior state-of-the-art methods across multiple key metrics.
Beibei Jing, Tianle Guo, Youjia Zhang +5
Huazhong University of Science and Technology School of Computer Science and Technology Wuhan, Hubei, China · Zhejiang University School of Software Technology Ningbo, China