18 accepted papers
In cooperative video games, traditional AI companions are deployed to assist players, who control them using hotkeys or command wheels to issue predefined commands such as
Text-conditioned human motion in-betweening leverages keyframes for spatio-temporal control, with text providing high-level semantic guidance for the transitions. However, existing methods are unable to establish a coherent alignment between textual semantics and the spatio-temporal constraints prov
Sewing patterns, the essential blueprints for fabric cutting and tailoring, act as a crucial bridge between design concepts and producible garments. However, existing uni-modal sewing pattern generation models struggle to effectively encode complex design concepts with a multi-modal nature and corre…
Numerous researches on real-time motion generation primarily focus on kinematic aspects, often resulting in physically implausible outcomes. In this paper, we present POMP ("\underline P hysics-c\underline O nsistent Human \underline M otion \underline P rior through Phase Manifolds"), a novel kinem…
Using parts of existing models to rebuild new models, commonly termed as example-based modeling, is a classical methodology in the realm of computer graphics. Previous works mostly focus on shape composition, making them very hard to use for realistic composition of 3D objects captured from real-wor…
A variety of Neural Radiance Fields (NeRF) methods have recently achieved remarkable success in high render speed. However, current accelerating methods are specialized and incompatible with various implicit methods, preventing real-time composition over various types of NeRF works. Because NeRF rel…
Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction. Nonetheless cutting-edge dynamic neural rendering methods rely heavily on these implicit representations which frequently struggle to capture the intricate details of objects in the scene. Furthermor…
Dichotomous Image Segmentation (DIS) tasks require highly precise annotations, and traditional dataset creation methods are labor intensive, costly, and require extensive domain expertise. Although using synthetic data for DIS is a promising solution to these challenges, current generative models an…
Implicit representation has opened up new possibilities for inverse rendering. However, existing implicit neural inverse rendering methods struggle to handle strongly illuminated scenes with significant shadows and slight reflections. The existence of shadows and reflections can lead to an inaccurat…
Face alignment is a critical and difficult task for many facial analysis applications. Existing VFA methods frequently ignore the consistency of facial geometries and textures across video sequences, limiting their ability to handle accurate and stable face alignment. This paper describes a robust a…
Pedestrian trajectory prediction is the key technology in many applications for providing insights into human behavior and anticipating human future motions. Most existing empirical models are explicitly formulated by observed human behaviors using explicable mathematical terms with deterministic na…
The recent advancements in 3D Gaussian splatting (3D-GS) have not only facilitated real-time rendering through modern GPU rasterization pipelines but have also attained state-of-the-art rendering quality. Nevertheless, despite its exceptional rendering quality and performance on standard datasets, 3…
Existing face generators exhibit exceptional performance on faces in small to medium poses (with respect to frontal faces) but struggle to produce realistic results for large poses. The distorted rendering results on large poses in 3D-aware generators further show that the generated 3D face shapes a…
"Self-supervised monocular depth estimation has achieved promising performance recently. A consensus is that high-resolution inputs often yield better results. However, we find that the performance gap between high and low resolutions lies in the inappropriate feature representation of the widely us…
Removing hair from portrait images is challenging due to the complex occlusions between hair and face, as well as the lack of paired portrait data with/without hair. To this end, we present a dataset and a baseline method for removing hair from portrait images using generative adversarial networks (…
We present TraEDITS, a novel traffic trajectory editing framework for autonomous vehicle testing, which can generate new traffic behaviors by controlling each vehicle interactively to increase the diversity or irregularity of traffic testing data. Given a traffic flow with its original trajectories,
Recent failures in real-world self-driving tests have suggested a paradigm shift from directly learning in real-world roads to building a high-fidelity driving simulator as an alternative, effective, and safe tool to handle intricate traffic environments in urban areas. To date, traffic simulation c…
We present a general solution for steering microrobotic swarm by dynamic actuating fields. In our approach, the motion of micro-robots is controlled by changing the actuating direction of a field applied to them. The time-series sequence of actuating field's directions can be computed automatically.