LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior
We present LARP, a novel video tokenizer designed to overcome limitations in
current video tokenization methods for autoregressive (AR) generative models.
Unlike traditional patchwise tokenizers that directly encode local visual
patches into discrete tokens, LARP introduces a holistic tokenization scheme
that gathers information from the visual content using a set of learned
holistic queries. This design allows LARP to capture more global and semantic
representations, rather than being limited to local patch-level information.
Furthermore, it offers flexibility by supporting an arbitrary number of
discrete tokens, enabling adaptive and efficient tokenization based on the
specific requirements of the task. To align the discrete token space with
downstream AR generation tasks, LARP integrates a lightweight AR transformer as
a training-time prior model that predicts the next token on its discrete latent
space. By incorporating the prior model during training, LARP learns a latent
space that is not only optimized for video reconstruction but is also
structured in a way that is more conducive to autoregressive generation.
Moreover, this process defines a sequential order for the discrete tokens,
progressively pushing them toward an optimal configuration during training,
ensuring smoother and more accurate AR generation at inference time.
Comprehensive experiments demonstrate LARP's strong performance, achieving
state-of-the-art FVD on the UCF101 class-conditional video generation
benchmark. LARP enhances the compatibility of AR models with videos and opens
up the potential to build unified high-fidelity multimodal large language
models (MLLMs).
67203e5340b7cfa09fbc4b11
Hanyu Wang
claimed_verified
2024-10-30T09:54:29.660Z
6365a174ad2c9e8b6731dd0f
/avatars/2f4dd0eda92bca5a7464129fe7d961f9.svg
Hanyu Wang
user
hywang66
67203e5340b7cfa09fbc4b12
Saksham Suri
67203e5340b7cfa09fbc4b13
Yixuan Ren
67203e5340b7cfa09fbc4b14
Hao Chen
67203e5340b7cfa09fbc4b15
Abhinav Shrivastava
LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior
We present LARP, a novel video tokenizer designed to overcome limitations in
current video tokenization methods for autoregressive (AR) generative models.
Unlike traditional patchwise tokenizers that directly encode local visual
patches into discrete tokens, LARP introduces a holistic tokenization scheme
that gathers information from the visual content using a set of learned
holistic queries. This design allows LARP to capture more global and semantic
representations, rather than being limited to local patch-level information.
Furthermore, it offers flexibility by supporting an arbitrary number of
discrete tokens, enabling adaptive and efficient tokenization based on the
specific requirements of the task. To align the discrete token space with
downstream AR generation tasks, LARP integrates a lightweight AR transformer as
a training-time prior model that predicts the next token on its discrete latent
space. By incorporating the prior model during training, LARP learns a latent
space that is not only optimized for video reconstruction but is also
structured in a way that is more conducive to autoregressive generation.
Moreover, this process defines a sequential order for the discrete tokens,
progressively pushing them toward an optimal configuration during training,
ensuring smoother and more accurate AR generation at inference time.
Comprehensive experiments demonstrate LARP's strong performance, achieving
state-of-the-art FVD on the UCF101 class-conditional video generation
benchmark. LARP enhances the compatibility of AR models with videos and opens
up the potential to build unified high-fidelity multimodal large language
models (MLLMs).
LARP, a holistic video tokenizer with learned queries, enhances autoregressive generative models for videos through adaptive tokenization and a lightweight AR transformer, achieving state-of-the-art performance on class-conditional video generation.
video tokenizer autoregressive generative models holistic tokenization holistic queries global and semantic representations flexible tokenization discrete tokens lightweight AR transformer training-time prior model latent space sequential order AR generation state-of-the-art FVD UCF101 class-conditional video generation high-fidelity multimodal large language models