We introduce AvatarPointillist, a novel framework for generating dynamic 4D Gaussian avatars from a single portrait image. At the core of our method is a decoder-only Transformer that autoregressively generates a point cloud for 3D Gaussian Splatting. This sequential approach allows for precise, adaptive construction, dynamically adjusting point density and the total number of points based on the subject's complexity. During point generation, the AR model also jointly predicts per-point binding information, enabling realistic animation. After generation, a dedicated Gaussian decoder converts the points into complete, renderable Gaussian attributes. We demonstrate that conditioning the decoder on the latent features from the AR generator enables effective interaction between stages and markedly improves fidelity. Extensive experiments validate that AvatarPointillist produces high-quality, photorealistic, and controllable avatars. We believe this autoregressive formulation represents a new paradigm for avatar generation, and we will release our code inspire future research.
Figures & tables
Figure 2: Comparison of different Gaussian point cloud modeling approaches. LAM [ 22 ] constructs Gaussian point clouds based on a point cloud template, which fails to reconstruct fine details from the image, such as ponytails. In contrast, our method utilizes an AR model to directly model the Gaussian point cloud. It effectively learns the capability to adaptively adjust point density and count, enabling precise modeling. Moreover, we also include final rendering results for comparison. LAM produces distorted geometry and shows noticeable artifacts.
Figure 3: Overview of our framework. It consists of two modules: an autoregressive (AR) model for Gaussian geometry generation and a Gaussian Decoder for predicting rendering attributes. The AR model takes image features from DINOv2 [ 50 ] and point cloud features as input. The point cloud feature extract via Pixel3DMM [ 15 ] and a point cloud encoder [ 85 ] . The AR model is trained to generate a Gaussian point cloud via next-token prediction, where each point is represented by four quantized tokens (Tnx,Tny,Tnz,Tnb) corresponding to coordinates and binding information. After generation, the tokens are de-quantized to obtain the actual coordinates. We then combine the positional embeddings Pn with the internal features Fnp from the AR model as input to the Gaussian Decoder to predict the final accurate Gaussian attributes. Finally, the result is animated using Linear Blend Skinning (LBS) and the binding information.
Figure 4: Qualitative comparison with state-of-the-art methods. The leftmost column shows the input images, with the target image displayed in the bottom-right corner. The first row presents self-reenactment results, while the remaining three rows show cross-reenactment results. Our method demonstrates superior performance in expression and pose consistency, as well as better identity preservation compared to other approaches.
Method
Self reenactment
Cross reenactment
LPIPS ↓
FID ↓
AKD ↓
APD ↓
FID ↓
CLIP ↑
AKD ↓
APD ↓
Portrait4Dv2 [ 10 ]
0.20
123.02
5.32
34.53
191.13
0.63
11.94
142.93
AvatarArtist [ 39 ]
0.21
118.94
6.87
39.58
175.69
0.61
9.32
187.31
LAM [ 22 ]
0.24
136.01
4.37
61.83
238.54
0.54
6.68
210.23
GAGAvatar [ 6 ]
0.18
111.76
3.93
27.94
181.22
0.71
10.01
170.12
Ours
0.15
95.18
2.38
22.86
160.74
0.75
5.93
153.13
Table 1: Quantitative evaluation of state-of-the-art methods and our approach on the NeRSemble dataset [ 32 ] . ↓ indicates lower is better while ↑ indicates higher is better. Red highlights the best result, and Blue highlights the second-best result.
Figure 5: Visualization of ablation study on input setting of Gaussian decoder. The leftmost column shows the input. The FLAME Positions baseline, similar to the LAM method, uses the canonical FLAME mesh vertices as a template and only applies decoder-predicted offsets to deform this template into a final Gaussian point cloud. Pointwise AR Feature refers to using only the AR features ( Fnp ) without positional information, while Positional Encoding uses only the point embeddings ( Pn ) without AR features.
Method
LPIPS ↓
FID ↓
AKD ↓
APD ↓
Flame Position
0.23
120.34
4.82
41.22
AR Feature
0.22
110.93
5.89
32.96
AR Position
0.19
103.80
5.81
41.49
Ours
0.15
95.18
2.38
22.86
Table 2: Ablaiton study on the NeRSemble dataset [ 32 ] . ↓ indicates lower is better while ↑ indicates higher is better.
The generation of high-fidelity, animatable 3D human avatars remains a core challenge in computer graphics and vision, with applications in VR, telepresence, and entertainment. Existing approaches based on implicit representations like NeRFs suffer from slow rendering and dynamic inconsistencies, while 3D Gaussian Splatting (3DGS) methods are typically limited to static head generation, lacking dynamic control. We bridge this gap by introducing AGORA, a novel framework that extends 3DGS within a generative adversarial network to produce animatable avatars. Our key contribution is a lightweight, FLAME-conditioned deformation branch that predicts per-Gaussian residuals, enabling identity-preserving, fine-grained expression control while allowing real-time inference. Identity is further preserved through spatial shape conditioning of the identity branch, and expression fidelity is enforced via a dual-discriminator training scheme leveraging synthetic renderings of the parametric mesh. AGORA generates avatars that are not only visually realistic but also precisely controllable. Quantitatively, we outperform state-of-the-art NeRF-based methods on expression accuracy while rendering at 250 FPS on a single GPU and, notably, at ∼9 FPS under CPU-only inference -- to our knowledge the first demonstration of CPU-only animatable 3DGS avatar synthesis. This work represents a significant step toward practical, high-performance digital humans. Project website: https://ramazan793.github.io/AGORA/
Ramazan Fazylov, Sergey Zagoruyko, Aleksandr Parkin +2
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Polynome AI, Dubai, UAE · MTS AI, Moscow, Russia
For full-body avatars, modeling surface dynamics is crucial for overcoming the uncanny valley and achieving perceptual realism. Person-agnostic methods recover static 3D avatars from monocular images, videos, or text prompts, but their skeleton-driven animations lack realistic surface dynamics such as clothing wrinkles. In contrast, person-specific methods achieve high-quality rendering and realistic dynamics, but require expensive multi-view captures for each individual. Recent generalizable dynamic avatar methods struggle to embed surface dynamics, leading to either limited multi-view consistency or dynamic expressiveness. To this end, we propose AvatarDynamizer, a generative method that transforms an off-the-shelf static 3D avatar into a controllable, realistic, and multi-view-consistent 4D avatar. We introduce a novel texture-space surface-dynamics embedding and formulate avatar dynamics modeling as conditional texture generation. Our encoder--decoder representation embeds pose-dependent dynamics into dynamic texture maps, enabling compatibility with pre-trained video diffusion models while decoding them into 3D Gaussians for multi-view consistent rendering. Since existing datasets are limited in scale, sequence length, or motion diversity, we collect a large-scale multi-view dataset with long sequences covering diverse skeletal motions and surface dynamics. Experiments show that our method effectively animates static avatars with faithful surface dynamics and outperforms competing generalizable methods in visual fidelity, especially under limited dynamic training data.
Guoxing Sun, Heming Zhu, Linjie Lyu +3
Max Planck Institute for Informatics, Saarland Informatics Campus · EPFL · VIA Research Center
We present FFAvatar, a Transformer-based 3D Gaussian framework for fast construction of high-quality and animatable 4D head avatars from one or more reference portrait images. Unlike existing feed-forward approaches that require a fixed number of input views, FFAvatar supports incremental reconstruction, progressively refining the avatar representation as additional reference images become available. At the core of our method is an alternating attention mechanism that disentangles identity appearance from expression and viewpoint variations, enabling the reconstruction of a canonical 3D appearance that remains consistent across poses and facial expressions. To balance visual fidelity and computational efficiency, we introduce a sparse-to-dense learning paradigm. Coarse appearance features are first learned using sparse primitives anchored to the FLAME vertex level and are subsequently densified in the UV domain to capture fine-grained geometric and texture details. We further propose a plug-and-play motion refinement module that enables subject-specific dynamic personalization by modeling residual motion beyond parametric deformation. Extensive experiments demonstrate that FFAvatar efficiently produces high-fidelity and controllable 4D head avatars, achieving superior flexibility, driving efficiency, and identity-consistent rendering across diverse expressions and viewpoints.
Jianjiang Yao, Ke Xian, Renxiang Dai +1
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China