SpinMeRound: Consistent Multi-View Identity Generation Using Diffusion Models
On the map: Diffusion models → Audio, speech and music
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Audio, speech and music, one of 14 directions in Diffusion models. Builds on 66 of its 78 references that are in the map. First posted on arXiv 14 Apr 2025.
Builds on
66 of 78 references.
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38 more
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- 2025TurboPortrait3D: Single-step diffusion-based fast portrait novel-view synthesis
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- 2023Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation
- 2024Human Multi-View Synthesis from a Single-View Model:Transferred Body and Face Representations
- 2024DreamHead: Learning Spatial-Temporal Correspondence via Hierarchical Diffusion for Audio-driven Talking Head Synthesis
- 2024Identity Preserving 3D Head Stylization with Multiview Score Distillation
- 2024VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis
- 2025Joint Learning of Depth and Appearance for Portrait Image Animation