Jiu-Cheng Xie

Jie Wang, Jiu-Cheng Xie, Xianyan Li et al.

Constructing vivid 3D head avatars for given subjects and realizing a series of animations on them is valuable yet challenging. This paper presents GaussianHead, which models the actional human head with anisotropic 3D Gaussians. In our framework, a motion deformation field and multi-resolution tri-plane are constructed respectively to deal with the head's dynamic geometry and complex texture. Notably, we impose an exclusive derivation scheme on each Gaussian, which generates its multiple doppelgangers through a set of learnable parameters for position transformation. With this design, we can compactly and accurately encode the appearance information of Gaussians, even those fitting the head's particular components with sophisticated structures. In addition, an inherited derivation strategy for newly added Gaussians is adopted to facilitate training acceleration. Extensive experiments show that our method can produce high-fidelity renderings, outperforming state-of-the-art approaches in reconstruction, cross-identity reenactment, and novel view synthesis tasks. Our code is available at: https://github.com/chiehwangs/gaussian-head.

Jiateng Liu, Hao Gao, Jiu-Cheng Xie et al.

This paper presents an effective Gaussian management framework for high-fidelity scene reconstruction of appearance and geometry. Departing from recent Gaussian Splatting (GS) methods that rely on indiscriminate attribute assignment, our approach introduces a novel densification strategy called \emph{GauSep} that selectively activates Gaussian color or normal attributes. Together with a tailored rendering pipeline, termed \emph{Separate Rendering}, this strategy alleviates gradient conflicts arising from dual supervision and yields improved reconstruction quality. In addition, we develop \emph{GauRep}, an adaptive and integrated Gaussian representation that reduces redundancy both at the individual and global levels, effectively balancing model capacity and number of parameters. To provide reliable geometric supervision essential for effective management, we also introduce \emph{CoRe}, a novel surface reconstruction module that distills normal fields from the SDF branch to the Gaussian branch through a confidence mechanism. Notably, our management framework is model-agnostic and can be seamlessly incorporated into other architectures, simultaneously improving performance and reducing model size. Extensive experiments demonstrate that our approach achieves superior performance in reconstructing both appearance and geometry compared with state-of-the-art methods, while using significantly fewer parameters.

Jiu-Cheng Xie, Jun Yang, Wenqing Wang et al.

Face aging has received continuous research attention over the past two decades. Although previous works on this topic have achieved impressive success, two longstanding problems remain unsettled: 1) generating diverse and plausible facial aging patterns at the target age stage; 2) measuring the rationality of identity variation between the original portrait and its syntheses with age progression or regression. In this paper, we introduce ${\rm{DLAT}}^{\boldsymbol{+}}$ to realize Diverse and Lifespan Age Transformation on human faces, where the diversity jointly manifests in the transformation of facial textures and shapes. Apart from the diversity mechanism embedded in the model, multiple consistency restrictions are leveraged to keep it away from counterfactual aging syntheses. Moreover, we propose a new metric to assess the rationality of Identity Deviation under Age Gaps (IDAG) between the input face and its series of age-transformed generations, which is based on statistical laws summarized from plenty of genuine face-aging data. Extensive experimental results demonstrate the uniqueness and effectiveness of our method in synthesizing diverse and perceptually reasonable faces across the whole lifetime.