Follow Your Track: Precise Skeleton Animation Controlled by 3D Trajectories
- URL: http://arxiv.org/abs/2606.25344v1
- Date: Wed, 24 Jun 2026 03:18:10 GMT
- Title: Follow Your Track: Precise Skeleton Animation Controlled by 3D Trajectories
- Abstract summary: 4D generation aims to animate 3D objects with realistic motion, holding great promise for applications.<n>Existing methods typically decouple 3D asset generation from motion synthesis.<n>We present textttACT, a trajectory-conditioned framework for topology-general skeletal animation.
- Score: 93.39170866007431
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 4D generation aims to animate 3D objects with realistic motion, holding great promise for applications. Existing methods typically decouple 3D asset generation from motion synthesis: acquire a 3D asset, prepare a structural representation like mesh and Gaussians, and synthesize motion from text or video control signals. However, dense mesh and Gaussian representations incur high computational costs and are prone to temporal artifacts, limiting animation quality and duration to only short clips. Meanwhile, text lacks fine-grained spatial and temporal details such as timing and coordination, while video entangles motion with appearance and background. Together, these limitations result in 4D animations that suffer from poor temporal consistency, wrong identification, and limited controllability. We address these issues with \texttt{ACT}, a trajectory-conditioned framework for topology-general skeletal animation. ACT uses skeletons as a compact structured and compute-efficient representation and 3D point trajectories from monocular video as explicit motion guidance which provide detailed motion patterns without appearance entanglement. At the core of ACT is a Routed Trajectory Injector, which achieves accurate and robust trajectory-to-joint transfer through three complementary designs: prior-guided hard routing establishes precise skeleton-to-mesh correspondences, global routing enables holistic joint-track interaction for full-body motion awareness, and local windowed cross-attention enforces fine-grained temporal alignment, improving micro-timing and reducing motion misalignment across varying motion rates. Extensive experiments demonstrate that \texttt{ACT} significantly outperforms existing methods in fidelity and temporal consistency.
Related papers
- BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives [14.13122283409208]
BLARM predicts a temporally coherent animated mesh whose motion follows the video.<n>We represent animation using a compact set of learned, time-varying rigid motion components.<n> BLARM produces accurate and temporally stable animations while recovering compact, interpretable motion structure from monocular video.
arXiv Detail & Related papers (2026-08-31T17:21:54Z) - AnimaSpark: A Feed-Forward Method for Animating Arbitrary 3D Objects [9.201202105097991]
Current methods for category-agnostic animation generation exhibit critical limitations in inference speed, motion quality, and adherence to textual prompts.<n>This paper introduces AnimaSpark, a novel pipeline for category-agnostic 3D animation generation.<n>Our method achieves superior performance over existing state-of-the-art techniques across key metrics, including text-motion alignment, quality of motion, and computational efficiency.
arXiv Detail & Related papers (2026-06-09T15:25:39Z) - Tora3: Trajectory-Guided Audio-Video Generation with Physical Coherence [16.685702628989212]
Tora3 is a trajectory-guided AV generation framework that improves physical coherence by using object trajectories as a shared kinematic prior.<n>Specifically, we design a trajectory-aligned motion representation for video, a kinematic-audio alignment module driven by trajectory-derived second-order kinematic states, and a hybrid flow matching scheme.<n>Extensive experiments show that Tora3 improves motion realism, motion-sound synchronization, and overall AV generation quality over strong open-source baselines.
arXiv Detail & Related papers (2026-04-10T07:37:03Z) - IM-Animation: An Implicit Motion Representation for Identity-decoupled Character Animation [58.297199313494]
Implicit methods capture motion semantics directly from driving video, but suffer from identity leakage and entanglement between motion and appearance.<n>We propose a novel implicit motion representation that compresses per-frame motion into compact 1D motion tokens.<n>Our methodology employs a three-stage training strategy to enhance the training efficiency and ensure high fidelity.
arXiv Detail & Related papers (2026-02-07T11:17:20Z) - In-2-4D: Inbetweening from Two Single-View Images to 4D Generation [63.68181731564576]
We propose a new problem, Inbetween-2-4D, for generative 4D (i.e., 3D + motion) in interpolate two single-view images.<n>In contrast to video/4D generation from only text or a single image, our interpolative task can leverage more precise motion control to better constrain the generation.
arXiv Detail & Related papers (2025-04-11T09:01:09Z) - Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better [61.381599921020175]
Temporal consistency is critical in video prediction to ensure that outputs are coherent and free of artifacts.<n>Traditional methods, such as temporal attention and 3D convolution, may struggle with significant object motion.<n>We propose the Tracktention Layer, a novel architectural component that explicitly integrates motion information using point tracks.
arXiv Detail & Related papers (2025-03-25T17:58:48Z) - Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches [12.221087476416056]
We introduce "motion patches", a new representation of motion sequences, and propose using Vision Transformers (ViT) as motion encoders via transfer learning.
These motion patches, created by dividing and sorting skeleton joints based on motion sequences, are robust to varying skeleton structures.
We find that transfer learning with pre-trained weights of ViT obtained through training with 2D image data can boost the performance of motion analysis.
arXiv Detail & Related papers (2024-05-08T02:42:27Z) - MotionBERT: A Unified Perspective on Learning Human Motion
Representations [46.67364057245364]
We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources.
We propose a pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observations.
We implement motion encoder with a Dual-stream Spatio-temporal Transformer (DSTformer) neural network.
arXiv Detail & Related papers (2022-10-12T19:46:25Z) - Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for
Temporal Sentence Grounding [61.57847727651068]
Temporal sentence grounding aims to localize a target segment in an untrimmed video semantically according to a given sentence query.
Most previous works focus on learning frame-level features of each whole frame in the entire video, and directly match them with the textual information.
We propose a novel Motion- and Appearance-guided 3D Semantic Reasoning Network (MA3SRN), which incorporates optical-flow-guided motion-aware, detection-based appearance-aware, and 3D-aware object-level features.
arXiv Detail & Related papers (2022-03-06T13:57:09Z) - Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in
the Wild [49.672487902268706]
We present a framework that jointly estimates camera temporal alignment and 3D point triangulation.
We reconstruct 3D motion trajectories of human bodies in events captured by multiple unsynchronized and unsynchronized video cameras.
arXiv Detail & Related papers (2020-07-24T23:50:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.