CIRCLE, a dataset containing 10 hours of full-body reach-ing motion from 5 subjects across nine scenes, paired with ego-centric information of the environment represented in various forms, such as RGBD videos. We use this dataset to train a model that generates human motion conditioned on scene information. Leveraging our dataset, the model

We introduce CIRCLE, a dataset of 3D whole body reaching motion and corresponding first person RGDB videos within a fully furnished virtual home. Synthesizing 3D human motion in a contextual, ecological environment is important for simulating realistic …

We present CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes, paired with ego-centric information of the environment represented in various forms, such as RGBD videos. We use this dataset to train a model that generates human motion conditioned on scene information.

2023年3月31日 · We present CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes, paired with ego-centric information of the environment represented in various forms, such as RGBD videos. We use this dataset to train a model that generates human motion conditioned on scene information.

Circle loss对基于类标签和基于对的标签都有一个统一的公式。 一个统一的视角 深度特征学习目的是最大化类内相似性 s_p ，同时最小化类间相似性。

We present CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes, paired with ego-centric information of the environment represented in various forms, such as RGBD videos. We use this dataset to train a model that generates human motion conditioned on scene information.

Inspired by marine animals that localize the positions of their com-panions underwater through echoes, we build a new angle-based trainable social interaction representation, named SocialCircle, for continuously reflecting the context of so-cial interactions at different angular orientations relative to the target agent.

2023年3月31日 · This work presents CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes, paired with ego-centric information of the environment represented in various forms, such as RGBD videos, to train a model that generates human motion conditioned on scene information.

Composed image retrieval (CIR) is the task of retrieving a target image specified by a query image and a relative text that describes a semantic modification to the query image. Existing methods in CIR struggle to accurately represent the image and the text modification, resulting in subpar performance.

We present CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes, paired with ego-centric information of the environment represented in various forms, such as RGBD videos. We use this dataset to train a model that generates human motion conditioned on scene information.