This paper addresses the challenge of reconstructing general articulated 3D objects from a single video. Existing methods using dynamic neural radiance fields have advanced the modeling of articulated objects like humans and animals, but struggle with piece-wise rigid objects due to limitations in deformation models. To tackle this, the authors propose Quasi-Rigid Blend Skinning (QRBS), a novel deformation model that enhances the rigidity of each part while maintaining flexible deformation at joints. The method combines three key approaches: an enhanced bone rigging system, quasi-sparse skinning weights, and geodesic point assignment. The primary insight is to define rigidity on bones rather than joints, improving the rigidity and motion integrity of components. The proposed method outperforms previous works in producing higher-fidelity 3D reconstructions of general articulated objects, as demonstrated on both real and synthetic datasets. The paper also includes a detailed introduction to related work, a method overview, and extensive experiments showing the effectiveness of REACTO.This paper addresses the challenge of reconstructing general articulated 3D objects from a single video. Existing methods using dynamic neural radiance fields have advanced the modeling of articulated objects like humans and animals, but struggle with piece-wise rigid objects due to limitations in deformation models. To tackle this, the authors propose Quasi-Rigid Blend Skinning (QRBS), a novel deformation model that enhances the rigidity of each part while maintaining flexible deformation at joints. The method combines three key approaches: an enhanced bone rigging system, quasi-sparse skinning weights, and geodesic point assignment. The primary insight is to define rigidity on bones rather than joints, improving the rigidity and motion integrity of components. The proposed method outperforms previous works in producing higher-fidelity 3D reconstructions of general articulated objects, as demonstrated on both real and synthetic datasets. The paper also includes a detailed introduction to related work, a method overview, and extensive experiments showing the effectiveness of REACTO.