The field of digital human modeling and animation is rapidly evolving, with a focus on creating more realistic and interactive digital humans. Recent developments have centered around improving the accuracy and control of facial animations, as well as enabling real-time interactions with digital humans. Notable advancements include the use of multimodal inputs, such as audio and text, to drive digital human interactions, and the development of novel architectures and loss functions to improve the fidelity and controllability of generated animations.
One of the common themes among the various research areas is the use of deep learning techniques to improve the accuracy and efficiency of 3D reconstruction and human modeling algorithms. For example, in the field of 3D object classification and reconstruction, topological data analysis and physics-informed neural networks have been used to improve classification accuracy and robustness. Similarly, in the field of 3D human reconstruction and generation, feed-forward models and unified frameworks have been proposed to reconstruct and generate high-quality 3D models from sparse and uncalibrated data.
Some notable papers in the area of digital human modeling and animation include X-Streamer, which introduces a unified framework for multimodal human world modeling, and StableDub, which proposes a novel framework for visual dubbing that integrates lip-habit-aware modeling with occlusion-robust synthesis. Other notable papers include SIE3D, which generates expressive 3D avatars from a single image and descriptive text, and 3DiFACE, which synthesizes and edits holistic 3D facial animation.
In the field of 3D reconstruction, notable papers include TACO-Net, which achieves state-of-the-art results in 3D object classification using topological signatures, and BFSM, which proposes a novel 3D bidirectional face-skull morphable model for joint face-skull reconstruction and analysis. Additionally, PAL-Net presents a fully automated deep learning pipeline for localizing 50 anatomical landmarks on 3D facial scans.
The field of human pose estimation and 3D reconstruction is also rapidly advancing, with the introduction of diffusion-based methods that have shown significant improvements in accuracy and robustness. Notable papers in this area include SDPose, which proposes a fine-tuning framework for human pose estimation using pre-trained diffusion models, and LieHMR, which introduces a novel approach for human mesh recovery using SO(3) diffusion.
Other research areas, such as 3D point cloud registration and LiDAR odometry, human activity recognition, and human movement analysis and medical imaging, are also making significant progress. Notable papers in these areas include An Adaptive ICP LiDAR Odometry Based on Reliable Initial Pose, SlotFM, and ProDA.
Overall, the field of digital human modeling and 3D reconstruction is rapidly evolving, with a focus on creating more realistic and interactive digital humans and improving the accuracy and efficiency of 3D reconstruction algorithms. The use of deep learning techniques and novel architectures is enabling significant advancements in these fields, with potential applications in areas such as computer vision, robotics, and medical imaging.
