BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives

• URL: http://arxiv.org/abs/2608.31113v1
• Date: Mon, 31 Aug 2026 17:21:54 GMT
• Title: BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives
• Abstract summary: 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.
• Score: 14.13122283409208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce BLARM, a feed-forward method for video-driven 3D mesh animation. Given a monocular video and a static object mesh, BLARM predicts a temporally coherent animated mesh whose motion follows the video. Rather than relying on explicit rigs or directly regressing high-dimensional vertex motion, we represent animation using a compact set of learned, time-varying rigid motion components and time-invariant vertex-to-component skinning weights. This yields a low-dimensional deformation space without requiring skeletons, cages, skinning weights, or rig annotations. Our architecture conditions geometry-derived deformation latents on video features through factorized spatial-temporal attention, then decodes rigid transformations blended by predicted skinning weights. Trained with trajectory reconstruction, entropy regularization, and motion-aware contrastive learning, BLARM produces accurate and temporally stable animations while recovering compact, interpretable motion structure from monocular video.

Related papers

• Follow Your Track: Precise Skeleton Animation Controlled by 3D Trajectories [93.39170866007431]
  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.
  arXiv  Detail & Related papers  (2026-06-24T03:18:10Z)
• Sketch2Motion: Text-driven 2D Sketch to 3D Animation via Diffusion-guided Skeleton Optimization [0.6445605125467574]
  Animation of 2D hand-drawn sketches provides an effective medium for visual communication.<n>Recent approaches to converting 2D sketches to 3D animations have mainly focused on specific types of motion.<n>We propose Sketch2Motion, a diffusion-guided framework for skeleton-based motion synthesis.
  arXiv  Detail & Related papers  (2026-05-27T12:32:07Z)
• Kinematics-Driven Gaussian Shape Deformation for Blurry Monocular Dynamic Scenes [32.40228197424996]
  We present Kinematics-GS, a kinematics-aware framework that models blur as motion-aligned deformation.<n>To stabilize optimization, we decompose scenes into dynamic and static components using temporal deformation variance.<n>We also introduce a challenging real-world dataset of deformable and elastic objects exhibiting non-rigid motion.
  arXiv  Detail & Related papers  (2026-05-09T03:01:17Z)
• 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)
• Recovering Dynamic 3D Sketches from Videos [30.87733869892925]
  Liv3Stroke is a novel approach for abstracting objects in motion with deformable 3D strokes.<n>We first extract noisy, 3D point cloud motion guidance from video frames using semantic features.<n>Our approach deforms a set of curves to abstract essential motion features as a set of explicit 3D representations.
  arXiv  Detail & Related papers  (2025-03-26T08:43:21Z)
• 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)
• Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach [54.559847511280545]
  We present a novel video generation framework that integrates 3-dimensional geometry and dynamic awareness.<n>To achieve this, we augment 2D videos with 3D point trajectories and align them in pixel space.<n>The resulting 3D-aware video dataset, PointVid, is then used to fine-tune a latent diffusion model.
  arXiv  Detail & Related papers  (2025-02-05T21:49:06Z)
• Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos [115.71874459429381]
  We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. Experiments on benchmark datasets demonstrate that our method outperforms previous methods for fast moving object deblurring and 3D reconstruction.
  arXiv  Detail & Related papers  (2021-11-29T11:25:14Z)
• Real-time Deep Dynamic Characters [95.5592405831368]
  We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance. We use a novel graph convolutional network architecture to enable motion-dependent deformation learning of body and clothing. We show that our model creates motion-dependent surface deformations, physically plausible dynamic clothing deformations, as well as video-realistic surface textures at a much higher level of detail than previous state of the art approaches.
  arXiv  Detail & Related papers  (2021-05-04T23:28:55Z)

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.