Zobrazeno 1 - 10
of 397
pro vyhledávání: '"Ren, JiaWei"'
Autor:
Ren, Jiawei, Xie, Kevin, Mirzaei, Ashkan, Liang, Hanxue, Zeng, Xiaohui, Kreis, Karsten, Liu, Ziwei, Torralba, Antonio, Fidler, Sanja, Kim, Seung Wook, Ling, Huan
We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second. Key to our success is a novel dataset of multiview videos containing cur
Externí odkaz:
http://arxiv.org/abs/2406.10324
Recent advancements in bird's eye view (BEV) representations have shown remarkable promise for in-vehicle 3D perception. However, while these methods have achieved impressive results on standard benchmarks, their robustness in varied conditions remai
Externí odkaz:
http://arxiv.org/abs/2405.17426
Autor:
Kong, Lingdong, Xu, Xiang, Ren, Jiawei, Zhang, Wenwei, Pan, Liang, Chen, Kai, Ooi, Wei Tsang, Liu, Ziwei
Efficient data utilization is crucial for advancing 3D scene understanding in autonomous driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully supervised methods. Addressing this, our study extends into semi-supervise
Externí odkaz:
http://arxiv.org/abs/2405.05258
Diffusion models generate images with an unprecedented level of quality, but how can we freely rearrange image layouts? Recent works generate controllable scenes via learning spatially disentangled latent codes, but these methods do not apply to diff
Externí odkaz:
http://arxiv.org/abs/2404.07178
Publikováno v:
Expert Systems with Applications (2024): 123431
Underwater acoustic target recognition is a difficult task owing to the intricate nature of underwater acoustic signals. The complex underwater environments, unpredictable transmission channels, and dynamic motion states greatly impact the real-world
Externí odkaz:
http://arxiv.org/abs/2402.11919
Publikováno v:
OCEANS 2023-Limerick. IEEE, 2023: 1-6
Recognizing underwater targets from acoustic signals is a challenging task owing to the intricate ocean environments and variable underwater channels. While deep learning-based systems have become the mainstream approach for underwater acoustic targe
Externí odkaz:
http://arxiv.org/abs/2402.12658
4D content generation has achieved remarkable progress recently. However, existing methods suffer from long optimization times, a lack of motion controllability, and a low quality of details. In this paper, we introduce DreamGaussian4D (DG4D), an eff
Externí odkaz:
http://arxiv.org/abs/2312.17142
Generating animation of physics-based characters with intuitive control has long been a desirable task with numerous applications. However, generating physically simulated animations that reflect high-level human instructions remains a difficult prob
Externí odkaz:
http://arxiv.org/abs/2312.17135
Text-driven motion generation has achieved substantial progress with the emergence of diffusion models. However, existing methods still struggle to generate complex motion sequences that correspond to fine-grained descriptions, depicting detailed and
Externí odkaz:
http://arxiv.org/abs/2312.15004
Autor:
Chen, Shoufa, Xu, Mengmeng, Ren, Jiawei, Cong, Yuren, He, Sen, Xie, Yanping, Sinha, Animesh, Luo, Ping, Xiang, Tao, Perez-Rua, Juan-Manuel
In this study, we explore Transformer-based diffusion models for image and video generation. Despite the dominance of Transformer architectures in various fields due to their flexibility and scalability, the visual generative domain primarily utilize
Externí odkaz:
http://arxiv.org/abs/2312.04557