The Wanderings of Odysseus in 3D Scenes
Our goal is to populate digital environments, in which digital humans have diverse body shapes, move perpetually, and have plausible body-scene contact. The core challenge is to generate realistic, controllable, and infinitely long motions for diverse 3D bodies. To this end, we propose generative motion primitives via body surface markers, or GAMMA in short. In our solution, we decompose the long-term motion into a time sequence of motion primitives. We exploit body surface markers and conditional variational autoencoder to model each motion primitive, and generate long-term motion by implementing the generative model recursively. To control the motion to reach a goal, we apply a policy network to explore the generative model's latent space and use a tree-based search to preserve the motion quality during testing. Experiments show that our method can produce more realistic and controllable motion than state-of-the-art data-driven methods. With conventional path-finding algorithms, the generated human bodies can realistically move long distances for a long period of time in the scene. Code is released for research purposes at: \url{https://yz-cnsdqz.github.io/eigenmotion/GAMMA/}
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Odysseus: Jailbreaking Commercial Multimodal LLM-integrated Systems via Dual Steganography
By integrating language understanding with perceptual modalities such as images, multimodal large language models (MLLMs) constitute a critical substrate for modern AI systems, particularly intelligent agents operating i…
Odyssey: Creation, Analysis and Detection of Trojan Models
Along with the success of deep neural network (DNN) models, rise the threats to the integrity of these models. A recent threat is the Trojan attack where an attacker interferes with the training pipeline by inserting tri…
Data PoisoningOdysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning
Given the rapidly growing capabilities of vision-language models (VLMs), extending them to interactive decision-making tasks such as video games has emerged as a promising frontier. However, existing approaches either re…
Reinforcement LearningAn Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System
One of the Greater London Authority's (GLA) response to the COVID-19 pandemic brings together multiple large-scale and heterogeneous datasets capturing mobility, transportation and traffic activity over the city of Londo…
Time SeriesTime Series AnalysisTime Series ForecastingFrom Programs to Poses: Factored Real-World Scene Generation via Learned Program Libraries
Real-world scenes, such as those in ScanNet, are difficult to capture, with highly limited data available. Generating realistic scenes with varied object poses remains an open and challenging task. In this work, we propo…
Scene Generation