GazeGen: Gaze-Driven User Interaction for Visual Content Generation
We present GazeGen, a user interaction system that generates visual content
(images and videos) for locations indicated by the user's eye gaze. GazeGen
allows intuitive manipulation of visual content by targeting regions of
interest with gaze. Using advanced techniques in object detection and
generative AI, GazeGen performs gaze-controlled image adding/deleting,
repositioning, and surface material changes of image objects, and converts
static images into videos. Central to GazeGen is the DFT Gaze (Distilled and
Fine-Tuned Gaze) agent, an ultra-lightweight model with only 281K parameters,
performing accurate real-time gaze predictions tailored to individual users'
eyes on small edge devices. GazeGen is the first system to combine visual
content generation with real-time gaze estimation, made possible exclusively by
DFT Gaze. This real-time gaze estimation enables various visual content
generation tasks, all controlled by the user's gaze. The input for DFT Gaze is
the user's eye images, while the inputs for visual content generation are the
user's view and the predicted gaze point from DFT Gaze. To achieve efficient
gaze predictions, we derive the small model from a large model (10x larger) via
novel knowledge distillation and personal adaptation techniques. We integrate
knowledge distillation with a masked autoencoder, developing a compact yet
powerful gaze estimation model. This model is further fine-tuned with Adapters,
enabling highly accurate and personalized gaze predictions with minimal user
input. DFT Gaze ensures low-latency and precise gaze tracking, supporting a
wide range of gaze-driven tasks. We validate the performance of DFT Gaze on AEA
and OpenEDS2020 benchmarks, demonstrating low angular gaze error and low
latency on the edge device (Raspberry Pi 4). Furthermore, we describe
applications of GazeGen, illustrating its versatility and effectiveness in
various usage scenarios.
672d8d9316766a76a7bbbd49
He-Yen Hsieh
672d8d9316766a76a7bbbd4a
Ziyun Li
672d8d9316766a76a7bbbd4b
Sai Qian Zhang
672d8d9316766a76a7bbbd4c
Wei-Te Mark Ting
672d8d9316766a76a7bbbd4d
Kao-Den Chang
672d8d9316766a76a7bbbd4e
Barbara De Salvo
672d8d9316766a76a7bbbd4f
Chiao Liu
672d8d9316766a76a7bbbd50
H. T. Kung
GazeGen: Gaze-Driven User Interaction for Visual Content Generation
We present GazeGen, a user interaction system that generates visual content
(images and videos) for locations indicated by the user's eye gaze. GazeGen
allows intuitive manipulation of visual content by targeting regions of
interest with gaze. Using advanced techniques in object detection and
generative AI, GazeGen performs gaze-controlled image adding/deleting,
repositioning, and surface material changes of image objects, and converts
static images into videos. Central to GazeGen is the DFT Gaze (Distilled and
Fine-Tuned Gaze) agent, an ultra-lightweight model with only 281K parameters,
performing accurate real-time gaze predictions tailored to individual users'
eyes on small edge devices. GazeGen is the first system to combine visual
content generation with real-time gaze estimation, made possible exclusively by
DFT Gaze. This real-time gaze estimation enables various visual content
generation tasks, all controlled by the user's gaze. The input for DFT Gaze is
the user's eye images, while the inputs for visual content generation are the
user's view and the predicted gaze point from DFT Gaze. To achieve efficient
gaze predictions, we derive the small model from a large model (10x larger) via
novel knowledge distillation and personal adaptation techniques. We integrate
knowledge distillation with a masked autoencoder, developing a compact yet
powerful gaze estimation model. This model is further fine-tuned with Adapters,
enabling highly accurate and personalized gaze predictions with minimal user
input. DFT Gaze ensures low-latency and precise gaze tracking, supporting a
wide range of gaze-driven tasks. We validate the performance of DFT Gaze on AEA
and OpenEDS2020 benchmarks, demonstrating low angular gaze error and low
latency on the edge device (Raspberry Pi 4). Furthermore, we describe
applications of GazeGen, illustrating its versatility and effectiveness in
various usage scenarios.
GazeGen uses a lightweight gaze estimation model to enable real-time, gaze-controlled manipulation of visual content, including generating images and videos.
object detection generative AI gaze-controlled image adding deleting repositioning surface material changes DFT Gaze real-time gaze predictions knowledge distillation masked autoencoder fine-tuned with Adapters low-latency gaze tracking AEA benchmark OpenEDS2020 benchmark Raspberry Pi 4