20 accepted papers
Modern camera pipelines apply extensive on-device processing, such as exposure adjustment, white balance, and color correction, which, while beneficial individually, often introduce photometric inconsistencies across views. These appearance variations violate multi-view consistency and degrade novel…
This paper presents a new dataset for Novel View Synthesis, generated from a high-quality, animated film with stunning realism and intricate detail. Our dataset captures a variety of dynamic scenes, complete with detailed textures, lighting, and motion, making it ideal for training and evaluating cu
Always-on sensing is essential for next-generation edge/wearable AI systems, yet continuous high-fidelity RGB video capture remains prohibitively expensive for resource-constrained mobile and edge platforms. We present a new paradigm for efficient streaming video understanding: grayscale-always, col
Current diffusion-based makeup transfer methods commonly use the makeup information encoded by off-the-shelf foundation models (e.g., CLIP) as condition to preserve the makeup style of reference image in the generation. Although effective, these works mainly have two limitations: (1) foundation mode
Large-scale pre-trained video generators offer powerful priors for Video Super-Resolution (VSR), yet adapting them remains computationally prohibitive. Full fine-tuning demands extensive resources, and ControlNet-style adapters lose their efficiency advantage under modern Diffusion Transformers (DiT…
Feed-forward 3D Gaussian Splatting (3DGS) models enable real-time scene generation but are hindered by suboptimal pixel-aligned primitive placement, which relies on a dense, rigid grid that limits both quality and efficiency. We introduce a new feed-forward architecture that detects 3D Gaussian prim
The performance of egocentric AI agents is fundamentally limited by multimodal intent ambiguity. This challenge arises from a combination of underspecified language, imperfect visual data, and deictic gestures, which frequently leads to task failure. Existing monolithic Vision-Language Models (VLMs)
We present CaricatureBooth, a system that transforms caricature creation into a simple interactive experience -- as easy as using a photo booth! A key challenge in caricature generation is two-fold: the scarcity of high-quality caricature data and the difficulty in enabling precise creative control…
Current training-free text-driven image translation primarily uses diffusion features (convolution and attention) of pre-trained model as guidance to preserve the style/structure of source image in translated image. However, the coarse guidance at feature level struggles with style (e.g., visual pat…
Generating realistic human motion videos is a pivotal challenge in advancing human-computer interaction. While existing approaches often focus on generating either head or gesture movements from audio, they lack unified control over full-body motion, frequently producing low-resolution and blurred o…
Dynamic Novel View Synthesis aims to generate photorealistic views of moving subjects from arbitrary viewpoints. This task is particularly challenging when relying on monocular video, where disentangling structure from motion is ill-posed and supervision is scarce. We introduce Video Diffusion-Aware…
We propose VideoHumanMIB, a novel framework for Video Human Motion In-betweening that enables seamless transitions between different motion video clips, facilitating the generation of longer and more natural digital human videos. While existing video frame interpolation methods work well for similar
Co-speech gestures if presented in the lively form of videos can achieve superior visual effects in human-machine interaction. While previous works mostly generate structural human skeletons resulting in the omission of appearance information we focus on the direct generation of audio-driven co-spee…
Audio-driven co-speech human gesture generation has made remarkable advancements recently. However, most previous works only focus on single person audio-driven gesture generation. We aim at solving the problem of conversational co-speech gesture generation that considers multiple participants in a…
For image super-resolution (SR) bridging the gap between the performance on synthetic datasets and real-world degradation scenarios remains a challenge. This work introduces a novel "Low-Res Leads the Way" (LWay) training framework merging Supervised Pre-training with Self-supervised Learning to enh…
Traditionally, 3d indoor datasets have generally prioritized scale over ground-truth accuracy in order to obtain improved generalization. However, using these datasets to evaluate dense geometry tasks, such as depth rendering, can be problematic as the meshes of the dataset are often incomplete and…
Capturing and preserving motion semantics is essential to motion retargeting between animation characters. However most of the previous works neglect the semantic information or rely on human-designed joint-level representations. Here we present a novel Semantics-aware Motion reTargeting (SMT) metho…
The art of communication beyond speech there are gestures. The automatic co-speech gesture generation draws much attention in computer animation. It is a challenging task due to the diversity of gestures and the difficulty of matching the rhythm and semantics of the gesture to the corresponding spee…
Speech-driven gesture generation is highly challenging due to the random jitters of human motion. In addition, there is an inherent asynchronous relationship between human speech and gestures. To tackle these challenges, we introduce a novel quantization-based and phase-guided motion matching framew…
"Top-down methods dominate the field of 3D human pose and shape estimation, because they are decoupled from human detection and allow researchers to focus on the core problem. However, cropping, their first step, discards the location information from the very beginning, which makes themselves unabl…