GUESS:GradUally Enriching SyntheSis for Text-Driven Human Motion Generation

Abstract: In this paper, we propose a novel cascaded diffusion-based generative framework for text-driven human motion synthesis, which exploits a strategy named GradUally Enriching SyntheSis (GUESS as its abbreviation). The strategy sets up generation objectives by grouping body joints of detailed skeletons in close semantic proximity together and then replacing each of such joint group with a single body-part node. Such an operation recursively abstracts a human pose to coarser and coarser skeletons at multiple granularity levels. With gradually increasing the abstraction level, human motion becomes more and more concise and stable, significantly benefiting the cross-modal motion synthesis task. The whole text-driven human motion synthesis problem is then divided into multiple abstraction levels and solved with a multi-stage generation framework with a cascaded latent diffusion model: an initial generator first generates the coarsest human motion guess from a given text description; then, a series of successive generators gradually enrich the motion details based on the textual description and the previous synthesized results. Notably, we further integrate GUESS with the proposed dynamic multi-condition fusion mechanism to dynamically balance the cooperative effects of the given textual condition and synthesized coarse motion prompt in different generation stages. Extensive experiments on large-scale datasets verify that GUESS outperforms existing state-of-the-art methods by large margins in terms of accuracy, realisticness, and diversity. Code is available at https://github.com/Xuehao-Gao/GUESS.

• The paper presents a cascaded diffusion-based framework that incrementally refines human motion synthesis from coarse abstractions to detailed actions based on text.
• It employs a latent conditional diffusion model combined with dynamic multi-condition fusion to balance textual cues with synthesized motion prompts at each refinement stage.
• Experimental results on HumanML3D and KIT-ML datasets show notable improvements in R-Precision and FID scores, enhancing accuracy, realism, and output diversity.

Exploration of GradUally Enriching SyntheSis for Text-Driven Human Motion Generation

The paper "GUESS: GradUally Enriching SyntheSis for Text-Driven Human Motion Generation" presents a novel approach towards the task of human motion synthesis driven by textual descriptions. The primary contribution is a cascaded diffusion-based generative framework termed as GUESS, which promises to significantly enhance the quality of text-driven motion generation by gradually enriching synthesis scales.

GUESS introduces a structured approach to human motion synthesis by breaking down the problem into multiple abstraction levels. Instead of generating detailed joint-based motion directly from text— a practice that faces challenges due to the disparities between textual and motion modalities—GUESS adopts a strategy of iteratively increasing the granularity of motion abstraction. It begins with a coarse generation capturing key motion characteristics and then progressively refines the granularity by enriching the motion’s details through a multilayered approach. This gradual refinement from coarse to fine helps stabilize the motion synthesis process and ensures better alignment with the cross-modal (text-to-motion) synthesis goals.

The proposed method integrates a latent conditional diffusion model that sequentially processes human motion at various levels using a multi-stage generation framework. Initially, the system generates a "coarse motion guess" from textual input, which serves as a baseline upon which further refinements are built. This refinement is achieved via successive generators, each responsible for adding more detail to the motion while being guided by both textual descriptions and previous synthetic results.

Further enhancing the GUESS framework is the incorporation of a dynamic multi-condition fusion mechanism. This mechanism adjusts how strongly textual conditions and synthesized coarse motion prompts influence each stage of generation dynamically. This fusion is critical, as it allows the model to give contextually appropriate weight to various input forms, optimizing synthesis across different stages.

The experimental validation on large-scale data evidences the effectiveness of GUESS, showing that it outperforms current state-of-the-art methods in accuracy, realism, and diversity by notable margins. This suggests that the structured processing and the multi-layer abstraction strategy provide a significant advantage in tackling the complexities of motion synthesis across modalities.

Numerical Results and Key Observations

The paper reports comprehensive evaluations, articulating GUESS’s superiority with marked improvements in R-Precision and FID scores, indicative of better text-motion alignment and realism. For instance, on the HumanML3D and KIT-ML datasets, the method not only displays improved retrieval accuracies but also illustrates heightened motion fidelity. Additionally, diversity and multimodality metrics underscore its capacity to produce varied outputs from identical textual descriptions, a critical facet in realistic motion generation.

Implications and Future Directions

The implications of GUESS extend into various domains such as virtual reality, gaming, and animation, where the need for high-quality, text-driven motion synthesis is paramount. Practically, GUESS could pave the way for more interactive and responsive virtual environments where user interaction can dynamically influence character motions.

Theoretically, the research sets the stage for further exploration into multi-stage generation strategies. Future work may explore adaptive strategies for selecting abstraction levels based on input specifics, or even broaden its applicability to incorporate other modalities like audio or real-time response systems.

In summary, GUESS embodies a sophisticated and structured approach to human motion synthesis, offering a fresh perspective and substantial results. It sets a solid foundation for advancing interactive AI, ensuring that the procedural relationship between input and generated outputs is both coherent and scalable. The paper's insights can propel the development of more nuanced and user-aligned generative models across various fields in artificial intelligence.