SpinMeRound: Consistent Multi-View Identity Generation Using Diffusion Models
This addresses the challenge of consistent multi-view identity generation for facial data, which is incremental as it builds on existing diffusion models.
The paper tackles the problem of generating realistic head portraits from novel viewpoints using diffusion models, achieving state-of-the-art results in 360-degree head synthesis.
Despite recent progress in diffusion models, generating realistic head portraits from novel viewpoints remains a significant challenge. Most current approaches are constrained to limited angular ranges, predominantly focusing on frontal or near-frontal views. Moreover, although the recent emerging large-scale diffusion models have been proven robust in handling 3D scenes, they underperform on facial data, given their complex structure and the uncanny valley pitfalls. In this paper, we propose SpinMeRound, a diffusion-based approach designed to generate consistent and accurate head portraits from novel viewpoints. By leveraging a number of input views alongside an identity embedding, our method effectively synthesizes diverse viewpoints of a subject whilst robustly maintaining its unique identity features. Through experimentation, we showcase our model's generation capabilities in 360 head synthesis, while beating current state-of-the-art multiview diffusion models.