This paper presents a system for synthesizing human-object interactions in contextual environments based on human-level instructions. The system consists of a high-level LLM planner and a low-level motion generator. The LLM planner interprets human-level instructions to generate target scene layouts and detailed task plans. The low-level motion generator then synthesizes synchronized object motion, full-body human motion, and detailed finger motion. The system addresses the challenge of generating realistic interactions by combining high-level planning with low-level motion synthesis. The high-level planner uses LLMs to derive spatial relationships between objects and generate target layouts. The low-level motion generator uses a multi-stage approach to generate realistic interactions, including initial motion generation, grasp pose optimization, motion refinement, and finger motion synthesis. The system is evaluated on various datasets and demonstrates the effectiveness of the approach in generating realistic human-object interactions. The results show that the system can generate realistic interactions for both small and large objects, with accurate contact and minimal penetration. The system is able to handle complex tasks such as setting up a workspace, moving objects, and manipulating large objects. The system is also able to generate long sequences of interactions with multiple objects, demonstrating its ability to handle complex and dynamic environments. The system's approach combines high-level planning with low-level motion synthesis to achieve realistic human-object interactions in contextual environments.This paper presents a system for synthesizing human-object interactions in contextual environments based on human-level instructions. The system consists of a high-level LLM planner and a low-level motion generator. The LLM planner interprets human-level instructions to generate target scene layouts and detailed task plans. The low-level motion generator then synthesizes synchronized object motion, full-body human motion, and detailed finger motion. The system addresses the challenge of generating realistic interactions by combining high-level planning with low-level motion synthesis. The high-level planner uses LLMs to derive spatial relationships between objects and generate target layouts. The low-level motion generator uses a multi-stage approach to generate realistic interactions, including initial motion generation, grasp pose optimization, motion refinement, and finger motion synthesis. The system is evaluated on various datasets and demonstrates the effectiveness of the approach in generating realistic human-object interactions. The results show that the system can generate realistic interactions for both small and large objects, with accurate contact and minimal penetration. The system is able to handle complex tasks such as setting up a workspace, moving objects, and manipulating large objects. The system is also able to generate long sequences of interactions with multiple objects, demonstrating its ability to handle complex and dynamic environments. The system's approach combines high-level planning with low-level motion synthesis to achieve realistic human-object interactions in contextual environments.