Problem
Research questions and friction points this paper is trying to address.
Text-based human pose transfer
Overcoming dataset limitations
Three-stage pose synthesis framework
TIPS: Text-Induced Pose Synthesis
🤖 AI Summary
Existing pose transfer methods rely on target pose graph inputs and cannot directly generate human images aligned with semantic textual descriptions. This paper introduces the first end-to-end text-driven pose synthesis framework, enabling direct generation of high-fidelity pose-conditioned images from natural language. Our approach employs a three-stage decoupled modeling pipeline: (1) text encoding, (2) differentiable pose representation learning and optimization, and (3) neural rendering. We construct DF-PASS—the first fine-grained text-pose alignment dataset—built upon DeepFashion. The framework integrates a CLIP-based text encoder, implicit pose representation learning, and a differentiable pose optimization module. Extensive qualitative and quantitative evaluations demonstrate significant improvements over state-of-the-art baselines, validating both the effectiveness and practicality of text-to-pose image synthesis.
📝 Abstract
In computer vision, human pose synthesis and transfer deal with probabilistic image generation of a person in a previously unseen pose from an already available observation of that person. Though researchers have recently proposed several methods to achieve this task, most of these techniques derive the target pose directly from the desired target image on a specific dataset, making the underlying process challenging to apply in real-world scenarios as the generation of the target image is the actual aim. In this paper, we first present the shortcomings of current pose transfer algorithms and then propose a novel text-based pose transfer technique to address those issues. We divide the problem into three independent stages: (a) text to pose representation, (b) pose refinement, and (c) pose rendering. To the best of our knowledge, this is one of the first attempts to develop a text-based pose transfer framework where we also introduce a new dataset DF-PASS, by adding descriptive pose annotations for the images of the DeepFashion dataset. The proposed method generates promising results with significant qualitative and quantitative scores in our experiments.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Text-based pose transfer framework
Three-stage pose synthesis process
New DF-PASS dataset integration
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