Hao Tang

Xiaokun Sun, Zhenyu Zhang, Ying Tai et al.

To integrate digital humans into everyday life, there is a strong demand for generating high-quality, fine-grained disentangled 3D avatars that support expressive animation and simulation capabilities, ideally from low-cost textual inputs. Although text-driven 3D avatar generation has made significant progress by leveraging 2D generative priors, existing methods still struggle to fulfill all these requirements simultaneously. To address this challenge, we propose DreamBarbie, a novel text-driven framework for generating animatable 3D avatars with separable shoes, accessories, and simulation-ready garments, truly capturing the iconic ``Barbie doll'' aesthetic. The core of our framework lies in an expressive 3D representation combined with appropriate modeling constraints. Unlike prior methods, we use G-Shell to uniformly model watertight components (e.g., bodies, shoes) and non-watertight garments. By reformulating boundaries as Euclidean field intersections instead of manifold geodesics, we propose an SDF-based initialization and a hole regularization loss that together achieve a 100x speedup and stable open topology without image input. These disentangled 3D representations are then optimized by specialized expert diffusion models tailored to each domain, ensuring high-fidelity outputs. To mitigate geometric artifacts and texture conflicts when combining different expert models, we further propose several effective geometric losses and strategies. Extensive experiments demonstrate that DreamBarbie outperforms existing methods in both dressed human and outfit generation. Our framework further enables diverse applications, including apparel combination, editing, expressive animation, and physical simulation. Project page: https://xiaokunsun.github.io/DreamBarbie.github.io/.

Songtao Li, Hao Tang

This survey provides a comprehensive overview of recent advances in multimodal alignment and fusion within the field of machine learning, driven by the increasing availability and diversity of data modalities such as text, images, audio, and video. Unlike previous surveys that often focus on specific modalities or limited fusion strategies, our work presents a structure-centric and method-driven framework that emphasizes generalizable techniques. We systematically categorize and analyze key approaches to alignment and fusion through both structural perspectives -- data-level, feature-level, and output-level fusion -- and methodological paradigms -- including statistical, kernel-based, graphical, generative, contrastive, attention-based, and large language model (LLM)-based methods, drawing insights from an extensive review of over 260 relevant studies. Furthermore, this survey highlights critical challenges such as cross-modal misalignment, computational bottlenecks, data quality issues, and the modality gap, along with recent efforts to address them. Applications ranging from social media analysis and medical imaging to emotion recognition and embodied AI are explored to illustrate the real-world impact of robust multimodal systems. The insights provided aim to guide future research toward optimizing multimodal learning systems for improved scalability, robustness, and generalizability across diverse domains.