TokBench: Evaluating Your Visual Tokenizer before Visual Generation
In this work, we reveal the limitations of visual tokenizers and VAEs in
preserving fine-grained features, and propose a benchmark to evaluate
reconstruction performance for two challenging visual contents: text and face.
Visual tokenizers and VAEs have significantly advanced visual generation and
multimodal modeling by providing more efficient compressed or quantized image
representations. However, while helping production models reduce computational
burdens, the information loss from image compression fundamentally limits the
upper bound of visual generation quality. To evaluate this upper bound, we
focus on assessing reconstructed text and facial features since they typically:
1) exist at smaller scales, 2) contain dense and rich textures, 3) are prone to
collapse, and 4) are highly sensitive to human vision. We first collect and
curate a diverse set of clear text and face images from existing datasets.
Unlike approaches using VLM models, we employ established OCR and face
recognition models for evaluation, ensuring accuracy while maintaining an
exceptionally lightweight assessment process <span style="font-weight: bold;
color: rgb(214, 21, 21);">requiring just 2GB memory and 4 minutes</span> to
complete. Using our benchmark, we analyze text and face reconstruction quality
across various scales for different image tokenizers and VAEs. Our results show
modern visual tokenizers still struggle to preserve fine-grained features,
especially at smaller scales. We further extend this evaluation framework to
video, conducting comprehensive analysis of video tokenizers. Additionally, we
demonstrate that traditional metrics fail to accurately reflect reconstruction
performance for faces and text, while our proposed metrics serve as an
effective complement.
6839d7b3ede0c6c6e33e9363
Junfeng Wu
6839d7b3ede0c6c6e33e9364
Dongliang Luo
6839d7b3ede0c6c6e33e9365
Weizhi Zhao
6839d7b3ede0c6c6e33e9366
Zhihao Xie
6839d7b3ede0c6c6e33e9367
Yuanhao Wang
6839d7b3ede0c6c6e33e9368
Junyi Li
claimed_verified
2025-09-19T06:51:36.421Z
63aaf2a2a4bdd629b7eb2b5b
https://cdn-avatars.huggingface.co/v1/production/uploads/63aaf2a2a4bdd629b7eb2b5b/WOa3nAUNy5D3MsFUV9B8Z.jpeg
Junyi Li
user
ProvenceStar
6839d7b3ede0c6c6e33e9369
Xudong Xie
6839d7b3ede0c6c6e33e936a
Yuliang Liu
6839d7b3ede0c6c6e33e936b
Xiang Bai
TokBench: Evaluating Your Visual Tokenizer before Visual Generation
In this work, we reveal the limitations of visual tokenizers and VAEs in
preserving fine-grained features, and propose a benchmark to evaluate
reconstruction performance for two challenging visual contents: text and face.
Visual tokenizers and VAEs have significantly advanced visual generation and
multimodal modeling by providing more efficient compressed or quantized image
representations. However, while helping production models reduce computational
burdens, the information loss from image compression fundamentally limits the
upper bound of visual generation quality. To evaluate this upper bound, we
focus on assessing reconstructed text and facial features since they typically:
1) exist at smaller scales, 2) contain dense and rich textures, 3) are prone to
collapse, and 4) are highly sensitive to human vision. We first collect and
curate a diverse set of clear text and face images from existing datasets.
Unlike approaches using VLM models, we employ established OCR and face
recognition models for evaluation, ensuring accuracy while maintaining an
exceptionally lightweight assessment process <span style="font-weight: bold;
color: rgb(214, 21, 21);">requiring just 2GB memory and 4 minutes</span> to
complete. Using our benchmark, we analyze text and face reconstruction quality
across various scales for different image tokenizers and VAEs. Our results show
modern visual tokenizers still struggle to preserve fine-grained features,
especially at smaller scales. We further extend this evaluation framework to
video, conducting comprehensive analysis of video tokenizers. Additionally, we
demonstrate that traditional metrics fail to accurately reflect reconstruction
performance for faces and text, while our proposed metrics serve as an
effective complement.
Evaluation of visual tokenizers and VAEs on fine-grained feature reconstruction, focusing on text and face, reveals limitations in preserving detailed visual content and highlights the need for specialized metrics.
visual tokenizers VAEs fine-grained features benchmarking text reconstruction facial feature reconstruction image compression information loss visual generation quality multimodal modeling OCR face recognition video tokenizers reconstruction metrics