# DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View Synthesis

arXiv: https://arxiv.org/abs/2312.13016 · HTML: https://mlsanity.com/paper/2312.13016/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 52 of its 61 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 52)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2012.00926/) (2020)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization](https://mlsanity.com/paper/2306.16928/) (2023)
- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis](https://mlsanity.com/paper/2110.08985/) (2021)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)

## Built on by (most cited first, 26 of 26)

- [X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention](https://mlsanity.com/paper/2403.15931/) (2024)
- [HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation](https://mlsanity.com/paper/2503.18860/) (2025)
- [CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View Diffusion Models](https://mlsanity.com/paper/2412.12093/) (2024)
- [Pippo: High-Resolution Multi-View Humans from a Single Image](https://mlsanity.com/paper/2502.07785/) (2025)
- [VOODOO XP: Expressive One-Shot Head Reenactment for VR Telepresence](https://mlsanity.com/paper/2405.16204/) (2024)
- [Joker: Conditional 3D Head Synthesis with Extreme Facial Expressions](https://mlsanity.com/paper/2410.16395/) (2024)
- [FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers](https://mlsanity.com/paper/2507.12956/) (2025)
- [DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis](https://mlsanity.com/paper/2503.15667/) (2025)
- [SOAP: Style-Omniscient Animatable Portraits](https://mlsanity.com/paper/2505.05022/) (2025)
- [DiTaiListener: Controllable High Fidelity Listener Video Generation with Diffusion](https://mlsanity.com/paper/2504.04010/) (2025)
- [Reference-Based 3D-Aware Image Editing with Triplanes](https://mlsanity.com/paper/2404.03632/) (2024)
- [HOFAR: High-Order Augmentation of Flow Autoregressive Transformers](https://mlsanity.com/paper/2503.08032/) (2025)
- [Controlling Avatar Diffusion with Learnable Gaussian Embedding](https://mlsanity.com/paper/2503.15809/) (2025)
- [SpinMeRound: Consistent Multi-View Identity Generation Using Diffusion Models](https://mlsanity.com/paper/2504.10716/) (2025)
- [Text-based Animatable 3D Avatars with Morphable Model Alignment](https://mlsanity.com/paper/2504.15835/) (2025)
- [Stable-Hair v2: Real-World Hair Transfer via Multiple-View Diffusion Model](https://mlsanity.com/paper/2507.07591/) (2025)
- [T2I-ConBench: Text-to-Image Benchmark for Continual Post-training](https://mlsanity.com/paper/2505.16875/) (2025)
- [AvatarTex: High-Fidelity Facial Texture Reconstruction from Single-Image Stylized Avatars](https://mlsanity.com/paper/2511.06721/) (2025)
- [Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures](https://mlsanity.com/paper/2605.04035/) (2026)
- [Multi-view Consistent 3D Gaussian Head Avatars 'without' Multi-view Generation](https://mlsanity.com/paper/2605.25220/) (2026)
- [TurboPortrait3D: Single-step diffusion-based fast portrait novel-view synthesis](https://mlsanity.com/paper/2510.23929/) (2025)
- [MagicView: Multi-View Consistent Identity Customization via Priors-Guided In-Context Learning](https://mlsanity.com/paper/2511.00293/) (2025)
- [StyleFusion360: View-Consistent Head Stylization via Adaptive Style Modulation](https://mlsanity.com/paper/2511.22411/) (2025)
- [Envision: Embodied Visual Planning via Goal-Imagery Video Diffusion](https://mlsanity.com/paper/2512.22626/) (2025)
- [You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image](https://mlsanity.com/paper/2603.01328/) (2026)
- [GeoFace: Consistent Multi-View Face Generation with Geometry-Constrained Diffusion](https://mlsanity.com/paper/2606.27659/) (2026)

## Abstract (arXiv)

We present DiffPortrait3D, a conditional diffusion model that is capable of synthesizing 3D-consistent photo-realistic novel views from as few as a single in-the-wild portrait. Specifically, given a single RGB input, we aim to synthesize plausible but consistent facial details rendered from novel camera views with retained both identity and facial expression. In lieu of time-consuming optimization and fine-tuning, our zero-shot method generalizes well to arbitrary face portraits with unposed camera views, extreme facial expressions, and diverse artistic depictions. At its core, we leverage the generative prior of 2D diffusion models pre-trained on large-scale image datasets as our rendering backbone, while the denoising is guided with disentangled attentive control of appearance and camera pose. To achieve this, we first inject the appearance context from the reference image into the self-attention layers of the frozen UNets. The rendering view is then manipulated with a novel conditional control module that interprets the camera pose by watching a condition image of a crossed subject from the same view. Furthermore, we insert a trainable cross-view attention module to enhance view consistency, which is further strengthened with a novel 3D-aware noise generation process during inference. We demonstrate state-of-the-art results both qualitatively and quantitatively on our challenging in-the-wild and multi-view benchmarks.