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MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting
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Dynamic scene reconstruction is a long-term challenge in the field of 3D vision. Recently, the emergence of 3D Gaussian Splatting has provided new insights into this problem. Although subsequent efforts rapidly extend static 3D Gaussian to dynamic scenes, they often lack explicit constraints on object motion, leading to optimization difficulties and performance degradation. To address the above issues, we propose a novel deformable 3D Gaussian splatting framework called MotionGS, which explores explicit motion priors to guide the deformation of 3D Gaussians. Specifically, we first introduce an optical flow decoupling module that decouples optical flow into camera flow and motion flow, corresponding to camera movement and object motion respectively. Then the motion flow can effectively constrain the deformation of 3D Gaussians, thus simulating the motion of dynamic objects. Additionally, a camera pose refinement module is proposed to alternately optimize 3D Gaussians and camera poses, mitigating the impact of inaccurate camera poses. Extensive experiments in the monocular dynamic scenes validate that MotionGS surpasses state-of-the-art methods and exhibits significant superiority in both qualitative and quantitative results. Project page: https://ruijiezhu94.github.io/MotionGS_page
Forward citations
Cited by 5 Pith papers
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StreamME: Simplify 3D Gaussian Avatar within Live Stream
StreamME reconstructs an animatable head avatar from a live monocular video in about five minutes by attaching 3D Gaussian points to a tracked face mesh and pruning unimportant points during training.
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4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans
A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.
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STD-GS: Exploring Frame-Event Interaction for SpatioTemporal-Disentangled Gaussian Splatting to Reconstruct High-Dynamic Scene
STD-GS disentangles background and dynamic objects by clustering frame appearance and event motion features, and uses event brightness and flow to supervise Gaussian rendering, improving high-dynamic scene reconstruction.
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VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction
A training-only Gaussian splatting loss, which renders predicted 3D semantics and motion into 2D camera views, improves semantic occupancy and scene flow prediction across several camera-based models.
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FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction
A dynamic-scene representation where Gaussian primitives live freely in 4D space-time with linear motion and Gaussian time windows achieves state-of-the-art novel-view quality on complex-motion benchmarks.
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