$\text{PartConcepts}$: A Unified Mechanism for Fine-Grained Part Localization and Generation

While text-to-image (T2I) diffusion models exhibit strong semantic disentanglement at the object level, they struggle to localize fine-grained object parts. Although the latent visual features in these models are sufficiently fine-grained, the text–image interaction (cross-attention) fails to effectively exploit this information. This results in coarse and ambiguous localization, particularly for spatially distinct components such as `left` and `right` limbs. To address this limitation, we introduce **PartConcepts, a mechanism that encodes textual part descriptions into compact, learnable representations**. These PartConcept tokens are trained to selectively attend to their corresponding part regions in the image. To evaluate the part-level understanding of PartConcept tokens, we first probe its effectiveness for *part segmentation*. We then evaluate whether the improved localization enables *fine-grained instruction following* in T2I generation. On the standard **Open-Vocabulary Part Segmentation (OVPS) benchmark, our method outperforms dedicated segmentation baselines**, establishing a new state-of-the-art. We further evaluate our method on the more complex Pascal-Part benchmark for *part instance segmentation*, which contains fine-grained spatial annotations (e.g. `left lower arm`). Here, we outperform all baselines by a very significant margin. Finally, our qualitative and quantitative results show that this strong localization of PartConcept tokens directly enables *fine-grained instruction following* in T2I generation: the textual attributes (e.g., color) *inherently* bind well to corresponding PartConcept tokens, without explicit attribute control mechanisms. This indicates that PartConcept tokens compose seamlessly with other text tokens, highlighting their applicability for **fine-grained text-based generative control**.