  We present DiffHuman, a probabilistic method for photorealistic 3D human
reconstruction from a single RGB image. Despite the ill-posed nature of this
problem, most methods are deterministic and output a single solution, often
resulting in a lack of geometric detail and blurriness in unseen or uncertain
regions. In contrast, DiffHuman predicts a probability distribution over 3D
reconstructions conditioned on an input 2D image, which allows us to sample
multiple detailed 3D avatars that are consistent with the image. DiffHuman is
implemented as a conditional diffusion model that denoises pixel-aligned 2D
observations of an underlying 3D shape representation. During inference, we may
sample 3D avatars by iteratively denoising 2D renders of the predicted 3D
representation. Furthermore, we introduce a generator neural network that
approximates rendering with considerably reduced runtime (55x speed up),
resulting in a novel dual-branch diffusion framework. Our experiments show that
DiffHuman can produce diverse and detailed reconstructions for the parts of the
person that are unseen or uncertain in the input image, while remaining
competitive with the state-of-the-art when reconstructing visible surfaces.
