R-Cyclic Diffuser: Reductive and Cyclic Latent Diffusion for 3D Clothed Human Digitalization

Recently, the authors of Zero-1-to-3 demonstrated that a latent diffusion model, pretrained with Internet-scale data, can not only address the single-view 3D object reconstruction task but can even attain SOTA results in it. However, when applied to the task of single-view 3D clothed human reconstruction, Zero-1-to-3 (and related models) are unable to compete with the corresponding SOTA methods in this field despite being trained on clothed human data. In this work, we aim to tailor Zero-1-to-3's approach to the single-view 3D clothed human reconstruction task in a much more principled and structured manner. To this end, we propose R-Cyclic Diffuser, a framework that adapts Zero-1-to-3's novel approach to clothed human data by fusing it with a pixel-aligned implicit model. R-Cyclic Diffuser offers a total of three new contributions. The first and primary contribution is R-Cyclic Diffuser's cyclical conditioning mechanism for novel view synthesis. This mechanism directly addresses the view inconsistency problem faced by Zero-1-to-3 and related models. Secondly, we further enhance this mechanism with two key features -Lateral Inversion Constraint and Cyclic Noise Selection. Both features are designed to regularize and restrict the randomness of outputs generated by a latent diffusion model. Thirdly, we show how SMPL-X body priors can be incorporated in a latent diffusion model such that novel views of clothed human bodies can be generated much more accurately. Our experiments show that R-Cyclic Diffuser is able to outperform current SOTA methods in single-(a) Groundtruth (b) Zero-1-to-3 (c) One-1-2-3-45 (d) IntegratedPIFu Figure 1. Results of Zero-1-to-3 [7], One-2-3-45 [6], and Integrat-edPIFu [2] when trained and tested on clothed human subjects.