Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Schwing, Alexander G."'
We introduce GoMAvatar, a novel approach for real-time, memory-efficient, high-quality animatable human modeling. GoMAvatar takes as input a single monocular video to create a digital avatar capable of re-articulation in new poses and real-time rende
Externí odkaz:
http://arxiv.org/abs/2404.07991
Open-world video instance segmentation is an important video understanding task. Yet most methods either operate in a closed-world setting, require an additional user-input, or use classic region-based proposals to identify never before seen objects.
Externí odkaz:
http://arxiv.org/abs/2404.03657
Autor:
Guo, Pengsheng, Hao, Hans, Caccavale, Adam, Ren, Zhongzheng, Zhang, Edward, Shan, Qi, Sankar, Aditya, Schwing, Alexander G., Colburn, Alex, Ma, Fangchang
In the realm of text-to-3D generation, utilizing 2D diffusion models through score distillation sampling (SDS) frequently leads to issues such as blurred appearances and multi-faced geometry, primarily due to the intrinsically noisy nature of the SDS
Externí odkaz:
http://arxiv.org/abs/2312.02189
In real-world scenarios, arbitrary interactions with the environment can often be costly, and actions of expert demonstrations are not always available. To reduce the need for both, offline Learning from Observations (LfO) is extensively studied: the
Externí odkaz:
http://arxiv.org/abs/2311.01331
Offline imitation from observations aims to solve MDPs where only task-specific expert states and task-agnostic non-expert state-action pairs are available. Offline imitation is useful in real-world scenarios where arbitrary interactions are costly a
Externí odkaz:
http://arxiv.org/abs/2311.01329
Autor:
Zhao, Xiaoming, Colburn, Alex, Ma, Fangchang, Bautista, Miguel Angel, Susskind, Joshua M., Schwing, Alexander G.
Rendering scenes observed in a monocular video from novel viewpoints is a challenging problem. For static scenes the community has studied both scene-specific optimization techniques, which optimize on every test scene, and generalized techniques, wh
Externí odkaz:
http://arxiv.org/abs/2310.08587
Autor:
Ghaffari, Saba, Saleh, Ehsan, Schwing, Alexander G., Wang, Yu-Xiong, Burke, Martin D., Sinha, Saurabh
Protein design, a grand challenge of the day, involves optimization on a fitness landscape, and leading methods adopt a model-based approach where a model is trained on a training set (protein sequences and fitness) and proposes candidates to explore
Externí odkaz:
http://arxiv.org/abs/2305.13650
Although reinforcement learning has found widespread use in dense reward settings, training autonomous agents with sparse rewards remains challenging. To address this difficulty, prior work has shown promising results when using not only task-specifi
Externí odkaz:
http://arxiv.org/abs/2210.09496
We propose learnable polyphase sampling (LPS), a pair of learnable down/upsampling layers that enable truly shift-invariant and equivariant convolutional networks. LPS can be trained end-to-end from data and generalizes existing handcrafted downsampl
Externí odkaz:
http://arxiv.org/abs/2210.08001
Recent work on 3D-aware image synthesis has achieved compelling results using advances in neural rendering. However, 3D-aware synthesis of face dynamics hasn't received much attention. Here, we study how to explicitly control generative model synthes
Externí odkaz:
http://arxiv.org/abs/2210.05825