RigPAPR: Rig-Based Animation of Static Neural Point Clouds from a Fixed-Viewpoint Video

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

RigPAPR is a novel method designed to animate static neural point cloud reconstructions, addressing common joint-boundary artifacts seen in existing Gaussian splatting or mesh-proxy techniques. These artifacts arise because individual primitives with fixed shapes cannot bend with surface deformations, leading to gaps and spikes. RigPAPR leverages Proximity Attention Point Rendering (PAPR), an interpolation-based representation where surface shape dynamically emerges from the spatial configuration of primitive sets, allowing natural re-formation under articulation. The system auto-rigs a static PAPR cloud and drives it using direct Linear Blend Skinning (LBS) from a single fixed-viewpoint video, eliminating the need for mesh proxies or pose-dependent corrections. Quantitatively, RigPAPR achieves 3+ dB PSNR improvement over baselines at novel views on synthetic subjects and delivers cleaner joint-boundary renderings on both synthetic and real-world captures, demonstrating robust generalization to unseen poses.

Key takeaway

For Machine Learning Engineers developing animatable 3D assets from single fixed-view videos, you should consider RigPAPR's interpolation-based point rendering. This approach inherently mitigates joint-boundary artifacts common with Gaussian splatting or mesh-proxy methods. It delivers significantly cleaner articulation and better generalization to novel views and poses. Implement a two-phase optimization, leveraging initial depth and 2D tracking priors, to achieve robust rigged animation. Be mindful that the quality of 2D scaffolding priors can influence recovered articulation.

Key insights

Interpolation-based neural point rendering inherently avoids joint-boundary artifacts in rigged animation, unlike fixed-shape primitives.

Principles

• Per-primitive fixed shapes break canonical tiling under LBS.
• Interpolation-based rendering dynamically adapts to deformation.
• Single fixed-view animation benefits from decaying depth/track priors.

Method

RigPAPR auto-rigs a PAPR cloud using a transient mesh proxy, then refines LBS joint rotations and skinning weights via a two-phase video-guided optimization with depth and 2D tracking priors.

In practice

• Adopt PAPR for cleaner articulated neural assets.
• Implement two-phase optimization for monocular video rigging.
• Use auto-riggers to initialize skeleton and weights.

Topics

• RigPAPR
• Neural Point Clouds
• 3D Animation
• Linear Blend Skinning
• Gaussian Splatting
• Monocular Video Animation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.