A Unified Diffusion Framework for Scene-aware Human Motion Estimation from Sparse Signals

• URL: http://arxiv.org/abs/2404.04890v1
• Date: Sun, 7 Apr 2024 09:15:45 GMT
• Title: A Unified Diffusion Framework for Scene-aware Human Motion Estimation from Sparse Signals
• Abstract summary: We introduce a new framework to combine rich contextual information provided by scenes to benefit full-body motion tracking from sparse observations. $textS2$Fusion first extracts the spatial-temporal relations residing in the sparse signals via a periodic autoencoder. $textS2$Fusion utilizes conditional diffusion to fuse scene geometry and sparse tracking signals to generate full-body scene-aware motions.
• Score: 47.72778485845676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating full-body human motion via sparse tracking signals from head-mounted displays and hand controllers in 3D scenes is crucial to applications in AR/VR. One of the biggest challenges to this task is the one-to-many mapping from sparse observations to dense full-body motions, which endowed inherent ambiguities. To help resolve this ambiguous problem, we introduce a new framework to combine rich contextual information provided by scenes to benefit full-body motion tracking from sparse observations. To estimate plausible human motions given sparse tracking signals and 3D scenes, we develop $\text{S}^2$Fusion, a unified framework fusing \underline{S}cene and sparse \underline{S}ignals with a conditional dif\underline{Fusion} model. $\text{S}^2$Fusion first extracts the spatial-temporal relations residing in the sparse signals via a periodic autoencoder, and then produces time-alignment feature embedding as additional inputs. Subsequently, by drawing initial noisy motion from a pre-trained prior, $\text{S}^2$Fusion utilizes conditional diffusion to fuse scene geometry and sparse tracking signals to generate full-body scene-aware motions. The sampling procedure of $\text{S}^2$Fusion is further guided by a specially designed scene-penetration loss and phase-matching loss, which effectively regularizes the motion of the lower body even in the absence of any tracking signals, making the generated motion much more plausible and coherent. Extensive experimental results have demonstrated that our $\text{S}^2$Fusion outperforms the state-of-the-art in terms of estimation quality and smoothness.

Related papers

• RASA: Disentangled Spatial-Motional Priors for Cross-Identity Character Animation [85.41886214825894]
  Cross-identity character animation aims to drive a target identity from a reference image to follow the motion of a source character from a driving video.<n>We introduce Reference-Aware Structural Alignment (RASA), a framework that disentangles spatial mapping from motion control.<n>Our work establishes a new paradigm showing disentangled spatial and motional priors are key to robust character animation.
  arXiv  Detail & Related papers  (2026-08-28T11:36:44Z)
• 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)
• ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes [55.32681167870698]
  Video-informed Pose Spaces (ViPS) is a feed-forward framework that discovers the latent distribution of valid articulations for auto-rigged meshes.<n>ViPS transfers generative video priors into a universal distribution over a given rig parameterization.<n>Our evaluations show that ViPS matches the performance of state-of-the-art methods trained on synthetic artist-created 4D data in both plausibility and diversity.
  arXiv  Detail & Related papers  (2026-04-19T21:21:11Z)
• $x^2$-Fusion: Cross-Modality and Cross-Dimension Flow Estimation in Event Edge Space [22.76310468233763]
  Estimating dense 2D optical flow and 3D scene flow is essential for dynamic scene understanding.<n>Recent work combines images, LiDAR, and event data to jointly predict 2D and 3D motion, yet most approaches operate in separate heterogeneous feature spaces.<n>We introduce $x2$-Fusion, which reframes multimodal fusion as representation unification.
  arXiv  Detail & Related papers  (2026-03-17T15:36:57Z)
• Controllable Egocentric Video Generation via Occlusion-Aware Sparse 3D Hand Joints [87.13154261503168]
  Motion-controllable video generation is crucial for egocentric applications in virtual reality and embodied AI.<n>Existing methods often struggle to achieve 3D-consistent fine-grained hand articulation.<n>We propose a novel framework that generates egocentric videos from a single reference frame.
  arXiv  Detail & Related papers  (2026-03-12T10:02:23Z)
• From Frames to Sequences: Temporally Consistent Human-Centric Dense Prediction [22.291273919939957]
  We develop a scalable synthetic data pipeline that generates human frames and motion-aligned sequences with pixel-accurate depth, normals, and masks.<n>We train a unified ViT-based dense predictor that injects an explicit geometric human prior via CSE embeddings.<n>Our two-stage training strategy, combining static pretraining with dynamic sequence supervision, enables the model first to acquire robust spatial representations and then refine temporal consistency across motion-aligned sequences.
  arXiv  Detail & Related papers  (2026-02-02T05:28:58Z)
• Breaking the Vicious Cycle: Coherent 3D Gaussian Splatting from Sparse and Motion-Blurred Views [40.70901994944635]
  We introduce CoherentGS, a framework for high-fidelity 3D reconstruction from sparse and blurry images.<n>Our key insight is to address these compound degradations using a dual-prior strategy.<n>CoherentGS significantly outperforms existing methods, setting a new state-of-the-art for this challenging task.
  arXiv  Detail & Related papers  (2025-12-11T07:36:35Z)
• FUSER: Feed-Forward MUltiview 3D Registration Transformer and SE(3)$^N$ Diffusion Refinement [39.19949818461193]
  F is the first feed-forward multiview registration transformer that processes all scans in a unified, compact latent space.<n>F predicts global poses without any pairwise estimation.<n>Experiments on 3DMatch, ScanNet and ArkitScenes demonstrate that our approach achieves the superior registration accuracy and outstanding computational efficiency.
  arXiv  Detail & Related papers  (2025-12-10T07:11:22Z)
• Forge4D: Feed-Forward 4D Human Reconstruction and Interpolation from Uncalibrated Sparse-view Videos [27.595035122927204]
  We present a feed-forward 4D human reconstruction and model that efficientlycalibrates temporally aligned representations from uncalibrated sparse-view videos.<n>For novel time, we design a novel motion prediction module to predict dense motions for each 3D Gaussian between two adjacent frames.<n>Experiments demonstrate the effectiveness of our model on both in-domain and out-of-domain datasets.
  arXiv  Detail & Related papers  (2025-09-29T02:47:14Z)
• OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting [78.70702961852119]
  OracleGS reconciles generative completeness with regressive fidelity for sparse view Gaussian Splatting.<n>Our approach conditions the powerful generative prior on multi-view geometric evidence, filtering hallucinatory artifacts while preserving plausible completions in under-constrained regions.
  arXiv  Detail & Related papers  (2025-09-27T11:19:32Z)
• Multi-Modal Graph Convolutional Network with Sinusoidal Encoding for Robust Human Action Segmentation [10.122882293302787]
  temporal segmentation of human actions is critical for intelligent robots in collaborative settings.<n>We propose a Multi-Modal Graph Convolutional Network (MMGCN) that integrates low-frame-rate (e.g., 1 fps) visual data with high-frame-rate (e.g., 30 fps) motion data.<n>Our approach outperforms state-of-the-art methods, especially in action segmentation accuracy.
  arXiv  Detail & Related papers  (2025-07-01T13:55:57Z)
• RobustSplat: Decoupling Densification and Dynamics for Transient-Free 3DGS [79.15416002879239]
  3D Gaussian Splatting has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling.<n>Existing methods struggle with accurately modeling scenes affected by transient objects, leading to artifacts in the rendered images.<n>We propose RobustSplat, a robust solution based on two critical designs.
  arXiv  Detail & Related papers  (2025-06-03T11:13:48Z)
• Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding [59.51535163599723]
  FreeGS is an unsupervised semantic-embedded 3DGS framework that achieves view-consistent 3D scene understanding without the need for 2D labels. We show that FreeGS performs comparably to state-of-the-art methods while avoiding the complex data preprocessing workload.
  arXiv  Detail & Related papers  (2024-11-29T08:52:32Z)
• Temporal Residual Jacobians For Rig-free Motion Transfer [45.640576754352104]
  We introduce Residual Temporal Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume access to any rigging or intermediate shapes, produces geometrically and temporally consistent motions, and can be used to transfer long motion sequences.
  arXiv  Detail & Related papers  (2024-07-20T18:29:22Z)
• Mixed Diffusion for 3D Indoor Scene Synthesis [55.94569112629208]
  We present MiDiffusion, a novel mixed discrete-continuous diffusion model architecture. We represent a scene layout by a 2D floor plan and a set of objects, each defined by its category, location, size, and orientation. Our experimental results demonstrate that MiDiffusion substantially outperforms state-of-the-art autoregressive and diffusion models in floor-conditioned 3D scene synthesis.
  arXiv  Detail & Related papers  (2024-05-31T17:54:52Z)
• RoHM: Robust Human Motion Reconstruction via Diffusion [58.63706638272891]
  RoHM is an approach for robust 3D human motion reconstruction from monocular RGB(-D) videos. It conditioned on noisy and occluded input data, reconstructs complete, plausible motions in consistent global coordinates. Our method outperforms state-of-the-art approaches qualitatively and quantitatively, while being faster at test time.
  arXiv  Detail & Related papers  (2024-01-16T18:57:50Z)
• You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos [56.676761067861236]
  Given an untrimmed video, temporal sentence grounding aims to locate a target moment semantically according to a sentence query. Previous respectable works have made decent success, but they only focus on high-level visual features extracted from decoded frames. We propose a new setting, compressed-domain TSG, which directly utilizes compressed videos rather than fully-decompressed frames as the visual input.
  arXiv  Detail & Related papers  (2023-03-14T12:53:27Z)
• Detachable Novel Views Synthesis of Dynamic Scenes Using Distribution-Driven Neural Radiance Fields [19.16403828672949]
  Representing and synthesizing novel views in real-world dynamic scenes from casual monocular videos is a long-standing problem. Our approach $textbfD$etach the background from the entire $textbfD$ynamic scene, which is called $textD4$NeRF. Our approach outperforms previous methods in rendering texture details and motion areas while also producing a clean static background.
  arXiv  Detail & Related papers  (2023-01-01T14:39:09Z)
• OTPose: Occlusion-Aware Transformer for Pose Estimation in Sparsely-Labeled Videos [21.893572076171527]
  We propose a method that leverages an attention mask for occluded joints and encodes temporal dependency between frames using transformers. We achieve state-of-the-art pose estimation results for PoseTrack 2017 and PoseTrack 2018 datasets.
  arXiv  Detail & Related papers  (2022-07-20T08:06:06Z)
• A Dual-Masked Auto-Encoder for Robust Motion Capture with Spatial-Temporal Skeletal Token Completion [13.88656793940129]
  We propose an adaptive, identity-aware triangulation module to reconstruct 3D joints and identify the missing joints for each identity. We then propose a Dual-Masked Auto-Encoder (D-MAE) which encodes the joint status with both skeletal-structural and temporal position encoding for trajectory completion. In order to demonstrate the proposed model's capability in dealing with severe data loss scenarios, we contribute a high-accuracy and challenging motion capture dataset.
  arXiv  Detail & Related papers  (2022-07-15T10:00:43Z)
• iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
  iSDF is a continuous learning system for real-time signed distance field reconstruction. It produces more accurate reconstructions and better approximations of collision costs and gradients.
  arXiv  Detail & Related papers  (2022-04-05T15:48:39Z)
• OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstruction [14.130915525776055]
  RGBD-based real-time dynamic 3D reconstruction suffers from inaccurate inter-frame motion estimation. We propose OcclusionFusion, a novel method to calculate occlusion-aware 3D motion to guide the reconstruction. Our technique outperforms existing single-view-based real-time methods by a large margin.
  arXiv  Detail & Related papers  (2022-03-15T15:09:01Z)
• 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)

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.