Fusion Embedding Advances Pose-Guided Person Image Synthesis

The arXiv preprint titled "Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion Model" (arXiv:2412.07333, revised 23 May 2026) presents a two-stage framework named FPDM that explicitly learns a fused source-pose embedding and uses it to condition a diffusion model, according to the paper. The authors report integrating an Image-Pose Fusion module (IPF) and a Source-Enhanced Pose Fusion procedure that aligns the fusion embedding with the target image embedding through contrastive InfoNCE training, per the arXiv text. The paper reports experiments on the DeepFashion and RWTH-PHOENIX-Weather 2014T datasets and states that the method achieves competitive quantitative and qualitative results compared to existing approaches, as discussed in the arXiv submission and summarized in a literature review on TheMoonlight.io.

Editorial analysis - technical context: The paper leverages a CLIP-based Vision Transformer to extract image-level embeddings for the source image, target pose, and target image, per the authors' description on arXiv. The IPF uses a Combiner-style module to fuse source and pose embeddings and applies contrastive learning to align the fusion vector with the true target-image embedding. The conditional diffusion stage, as described in the paper, introduces the learned fusion embedding into a U-Net diffusion backbone, with the source encoder providing key/value features in transformer blocks inside the denoiser, according to the paper and the Moonlight review. These elements combine contrastive representation learning with diffusion conditioning to prioritize texture fidelity.

Editorial analysis: In pose-guided person image synthesis, prior diffusion-based pipelines typically rely on implicit feature aggregation during denoising; the paper frames explicit fusion embedding alignment as a path to improving fine-grained texture and identity consistency, per the authors. Industry-pattern observations: Combining pre-trained multi-modal encoders (like CLIP) and contrastive objectives with conditional diffusion models is a growing pattern in generative vision work because it separates representation alignment from generation, which can yield more controllable outputs.

For practitioners: Watch for open-source code, checkpoints, and evaluation scripts to validate reported gains on DeepFashion and RWTH-PHOENIX-Weather 2014T, since the arXiv paper reports competitive results but replication details matter for adoption. Also monitor subsequent comparisons that isolate the impact of the contrastive fusion loss versus stronger conditioning schemes within denoisers.