26 accepted papers
Neural radiance fields and 3D Gaussian splatting (3DGS) have significantly advanced 3D reconstruction and novel view synthesis (NVS). Yet, achieving high-fidelity and view-consistent renderings directly from point clouds---without costly per-scene optimization---remains a core challenge. In this wor…
Sound-guided object segmentation has drawn considerable attention for its potential to enhance multimodal perception. Previous methods primarily focus on developing advanced architectures to facilitate effective audio-visual interactions, without fully addressing the inherent challenges posed by aud…
Character customization, or 'face crafting,' is a vital feature in role-playing games (RPGs), enhancing player engagement by enabling the creation of personalized avatars. Existing automated methods often struggle with generalizability across diverse game engines due to their reliance on the interme…
Human pose estimation aims to predict the location of body keypoints and enable various practical applications. However, existing research focuses solely on individuals with full physical bodies and overlooks those with limb deficiencies. As a result, current pose estimation methods cannot be genera…
Generating sewing patterns in garment design is receiving increasing attention due to its CG-friendly and flexible-editing nature. Previous sewing pattern generation methods have been able to produce exquisite clothing, but struggle to design complex garments with detailed control. To address these…
Accurately localizing audible objects based on audio-visual cues is the core objective of audio-visual segmentation. Most previous methods emphasize spatial or temporal multi-modal modeling, yet overlook challenges from ambiguous audio-visual correspondences--such as nearby visually similar but acou…
Existing audio-visual segmentation datasets typically focus on short-trimmed videos with only one pixel-map annotation for a per-second video clip. In contrast for untrimmed videos the sound duration start- and end-sounding time positions and visual deformation of audible objects vary significantly.…
While image diffusion models have made significant progress in text-driven 3D content creation they often fail to accurately capture the intended meaning of text prompts especially for view information. This limitation leads to the Janus problem where multi-faced 3D models are generated under the gu…
"Generating human-object interactions (HOIs) is critical with the tremendous advances of digital avatars. Existing datasets are typically limited to humans interacting with a single object while neglecting the ubiquitous manipulation of multiple objects. Thus, we propose HIMO, a large-scale MoCap da…
Text-guided 3D face synthesis has achieved remarkable results by leveraging text-to-image (T2I) diffusion models. However most existing works focus solely on the direct generation ignoring the editing restricting them from synthesizing customized 3D faces through iterative adjustments. In this paper…
Predicting natural and diverse 3D hand gestures from the upper body dynamics is a practical yet challenging task in virtual avatar creation. Previous works usually overlook the asymmetric motions between two hands and generate two hands in a holistic manner, leading to unnatural results. In this wor…
Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. Compared with other biometric technologies, gait recognition is more difficult to disguise and can be applied to the condition of long-distance without the cooperation of subjects. Thus,…
Implicit neural representations have shown promising potential for 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it is, the computation of the information gain is expensive, and compared with that usi…
In this work, we propose a semantic flow-guided two-stage framework for shape-aware face swapping, namely FlowFace. Unlike most previous methods that focus on transferring the source inner facial features but neglect facial contours, our FlowFace can transfer both of them to a target face, thus lead…
Robust and efficient grasping of different objects is still an open problem due to the difficulty of integrating multidisciplinary knowledge such as gripper ontology design, perception, control, and learning. In recent years, learning-based methods have achieved excellent results in grasping various…
Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images. In order to achieve better decomposition, recent approaches attempt to model indirect illuminations reflected from different materials via Spherical Gaussians (SG), which, however, tends to blu…
Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, where a robot is required to explore a scene and plan a view path for the reconstru
Object-goal visual navigation aims at steering an agent toward an object via a series of moving steps. Previous works mainly focus on learning informative visual representations for navigation, but overlook the impacts of navigation states on the effectiveness and efficiency of navigation. We observ…
Learning surface by neural implicit rendering has been a promising way for multi-view reconstruction in recent years. Existing neural surface reconstruction methods, such as NeuS and VolSDF, can produce reliable meshes from multi-view posed images. Although they build a bridge between volume renderi…
Recent popular Role-Playing Games (RPGs) saw the great success of character auto-creation systems. The bone-driven face model controlled by continuous parameters (like the position of bones) and discrete parameters (like the hairstyles) makes it possible for users to personalize and customize in-gam…
In this paper, we present a novel double diffusion based neural radiance field, dubbed DD-NeRF, to reconstruct human body geometry and render the human body appearance in novel views from a sparse set of images. We first propose a double diffusion mechanism to achieve expressive representations of i…
Audio-driven one-shot talking face generation methods are usually trained on video resources of various persons. However, their created videos often suffer unnatural mouth shapes and asynchronous lips because those methods struggle to learn a consistent speech style from different speakers. We obser…
We propose an audio-driven talking-head method to generate photo-realistic talking-head videos from a single reference image. In this work, we tackle two key challenges: (i) producing natural head motions that match speech prosody, and (ii)} maintaining the appearance of a speaker in a large head mo…
One-shot talking face generation should synthesize high visual quality facial videos with reasonable animations of expression and head pose, and just utilize arbitrary driving audio and arbitrary single face image as the source. Current works fail to generate over 256 x 256 resolution realistic-look…
In this paper, we propose a novel text-based talking-head video generation framework that synthesizes high-fidelity facial expressions and head motions in accordance with contextual sentiments as well as speech rhythm and pauses. To be specific, our framework consists of a speaker-independent stage…
Our efforts are made on one-shot voice conversion where the target speaker is unseen in training dataset or both source and target speakers are unseen in the training dataset. In our work, StarGAN is employed to carry out voice conversion between speakers. An embedding vector is used to represent sp…