PixelSmile: Toward Fine-Grained Facial Expression Editing
Fine-grained facial expression editing has long been limited by intrinsic semantic overlap. To address this, we construct the Flex Facial Expression (FFE) dataset with continuous affective annotations and establish FFE-Bench to evaluate structural confusion, editing accuracy, linear controllability, and the trade-off between expression editing and identity preservation. We propose PixelSmile, a diffusion framework that disentangles expression semantics via fully symmetric joint training. PixelSmile combines intensity supervision with contrastive learning to produce stronger and more distinguishable expressions, achieving precise and stable linear expression control through textual latent interpolation. Extensive experiments demonstrate that PixelSmile achieves superior disentanglement and robust identity preservation, confirming its effectiveness for continuous, controllable, and fine-grained expression editing, while naturally supporting smooth expression blending.
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Jiabin Hua
claimed_verified
2026-03-27T11:33:42.865Z
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Jiabin Hua
Ammmob
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Ammmob
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Hengyuan Xu
claimed_verified
2026-03-27T11:33:44.832Z
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Hengyuan Xu
DobyXu
user
DobyXu
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Aojie Li
69c60a5e2de2ee20094edc2f
Wei Cheng
claimed_verified
2026-03-27T11:33:46.889Z
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Wei Cheng
wchengad
user
wchengad
69c60a5e2de2ee20094edc30
Gang Yu
69c60a5e2de2ee20094edc31
Xingjun Ma
69c60a5e2de2ee20094edc32
Yu-Gang Jiang
PixelSmile: Toward Fine-Grained Facial Expression Editing
Fine-grained facial expression editing has long been limited by intrinsic semantic overlap. To address this, we construct the Flex Facial Expression (FFE) dataset with continuous affective annotations and establish FFE-Bench to evaluate structural confusion, editing accuracy, linear controllability, and the trade-off between expression editing and identity preservation. We propose PixelSmile, a diffusion framework that disentangles expression semantics via fully symmetric joint training. PixelSmile combines intensity supervision with contrastive learning to produce stronger and more distinguishable expressions, achieving precise and stable linear expression control through textual latent interpolation. Extensive experiments demonstrate that PixelSmile achieves superior disentanglement and robust identity preservation, confirming its effectiveness for continuous, controllable, and fine-grained expression editing, while naturally supporting smooth expression blending.
A diffusion framework called PixelSmile is introduced that disentangles facial expression semantics through symmetric joint training and contrastive learning to enable precise, controllable, and fine-grained expression editing with robust identity preservation.
diffusion framework facial expression editing semantic overlap FFE dataset FFE-Bench PixelSmile joint training contrastive learning expression disentanglement textual latent interpolation expression blending