ARCH++: Animation-Ready Clothed Human Reconstruction Revisited (2108.07845v4)

Abstract: We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles. Our reconstructed avatars are animation-ready and highly realistic, in both the visible regions from input views and the unseen regions. While prior work shows great promise of reconstructing animatable clothed humans with various topologies, we observe that there exist fundamental limitations resulting in sub-optimal reconstruction quality. In this paper, we revisit the major steps of image-based avatar reconstruction and address the limitations with ARCH++. First, we introduce an end-to-end point based geometry encoder to better describe the semantics of the underlying 3D human body, in replacement of previous hand-crafted features. Second, in order to address the occupancy ambiguity caused by topological changes of clothed humans in the canonical pose, we propose a co-supervising framework with cross-space consistency to jointly estimate the occupancy in both the posed and canonical spaces. Last, we use image-to-image translation networks to further refine detailed geometry and texture on the reconstructed surface, which improves the fidelity and consistency across arbitrary viewpoints. In the experiments, we demonstrate improvements over the state of the art on both public benchmarks and user studies in reconstruction quality and realism.

• The paper introduces an end-to-end point-based geometry encoder using PointNet++ to enhance the semantic reconstruction of 3D clothed humans.
• It presents a co-supervising framework that jointly predicts occupancy in posed and canonical spaces, reducing ambiguity and improving surface fidelity.
• The approach leverages image-to-image translation to refine textures and geometry, achieving superior reconstruction quality across diverse benchmarks.

Overview of ARCH++: Revisiting Clothed Human Reconstruction for Animation

The paper presents ARCH++, a novel approach in the development of animation-ready 3D avatars from images featuring humans in arbitrary clothing styles and poses. ARCH++ is crafted to overcome the limitations of previous methodologies, improving on both the visible regions of the input and the unseen areas to ensure highly realistic reconstructions. Here, I provide an expert overview of the paper’s contributions, significant numerical results, and possible implications for the field of AI and computer vision.

ARCH++ tackles critical shortcomings identified in earlier research by introducing an innovative approach consisting of three main enhancements. First, ARCH++ replaces previous hand-crafted features with an end-to-end point-based geometry encoder. This adoption facilitates a more expressive understanding of the 3D human body semantics necessary for effective avatar creation. Utilization of PointNet++ enables extraction of semantic-aware geometry features directly from human shape and pose data, enhancing the encoder's capacity to accurately interpret complex human body topology and articulation.

Second, to address challenges associated with occupancy ambiguity resulting from topological changes inherent to clothed humans, the paper introduces a co-supervising framework. This framework enforces cross-space consistency to jointly predict occupancy in both posed and canonical spaces. This provides significant improvement in both occupied space determination and surface reconstruction fidelity. The dual-model prediction ensures that each space complements the weaknesses of the other, compensating for any distortion that might occur when a body is modeled in one space over the other.

Third, ARCH++ utilizes image-to-image translation networks to refine the detailed geometry and texture on the reconstructed surface. This strategy substantially increases both consistency and realism across a diverse range of viewpoints. This careful refinement of surface detail mitigates prior issues of degradation in areas obscured from view in the original image.

The experimental results demonstrate that ARCH++ achieves superior performance compared to state-of-the-art methods across diverse benchmarks in terms of reconstruction quality and realism. Analysis within the paper highlights a measurable error reduction in the numerical evaluation of occupancy estimation and reconstruction metrics, validating the efficacy of the approach.

The implications of these findings are significant in the progression towards fully photorealistic digital humans within the ever-expanding realms of Augmented and Virtual Reality (AR/VR). The demonstrated improvement in clothing detail and model realism has the potential to elevate user experiences in applications such as interactive gaming, virtual marketing, and digital social gatherings. Moreover, the methodological advancements presented in ARCH++ lay a promising groundwork for future research directions, including the integration of multiview inputs and potential adaptations to accommodate dynamic lighting and environmental simulations.

In conclusion, ARCH++ represents a meaningful step forward in the domain of image-based 3D avatar creation for animation. By addressing and surmounting historical challenges in clothed human reconstruction, it sets a new precedent for realistic and versatile avatar production, particularly relevant to digital content creation in interactive media. Future work may continue to expand upon these foundations, adopting broader datasets and incorporating additional environmental variables to achieve ever greater verisimilitude in the digital replication of the human form.