GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction

Sheng Zhao; Weikai Lin; Yuhao Zhu

cs.CVSep 29, 2026

GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction

Authors: Sheng Zhao, Weikai Lin, Yuhao Zhu

Organizations: Department of Computer Science University of Rochester

Abstract

Egocentric gaze prediction enables many downstream applications but remains challenging, as human gaze is inherently stochastic. This stochasticity is constrained by structured temporal dynamics alternating between fixations and saccades, top-down influences from tasks, and bottom-up visual saliency. Based on this observation, we introduce GazeFlow, a framework that directly models gaze as a joint distribution of temporal gaze positions conditioned upon both top-down and bottom-up information. In particular, GazeFlow uses conditional flow matching (CFM): a learned velocity field iteratively transports a Gaussian noise sample into a plausible gaze trajectory drawn from this joint distribution. The velocity field is conditioned on bottom-up visual features extracted by a video encoder and on top-down task information obtained by globally querying these features. On standard datasets, GazeFlow achieves state-of-the-art performance on per-frame metrics, and the generated trajectories align better with human gaze temporal dynamics.

Figures & tables

Figure. 1: Qualitative comparison on EGTEA Gaze+. Example frames where GazeFlow predicts a heatmap tightly localized on the ground-truth gaze positions (red dots) compared to existing methods (AT [ 17 ] , GLC [ 26 ] , EgoM2P [ 31 ] ).

				Human gaze mechanism
Method	Paradigm	Prediction	Training objective	Bottom-up	Top-down	Joint	Stochastic
Li et al. [32]	Regression	$\mathbf{g}_{t}\mid\mathcal{V}$	per-frame MSE	$\times$	$\sim$	$\times$	$\times$
GLC [ 26 ]	Marginal density estimation	$q_{\theta}(\mathbf{g}_{t}\mid\mathcal{V})$	per-frame KL	$\checkmark$	$\times$	$\times$	$\checkmark$
DFG [ 54 ]	Marginal density estimation	$q_{\theta}(\mathbf{g}_{t}\mid\mathcal{V})$	per-frame KL	$\checkmark$	$\times$	$\sim$	$\checkmark$
AT [ 17 ]	Autoregressive	$q_{\theta}(\mathbf{g}_{t}\mid\mathbf{g}_{t-1},\,\mathcal{V}_{\leq t})$	per-frame BCE	$\checkmark$	$\sim$	$\sim$	$\checkmark$
EgoM2P [ 31 ]	Autoregressive	$q_{\theta}(\mathbf{g}_{t}\mid\mathbf{g}_{<t},\,\mathcal{V})$	masked $L^{2}$	$\sim$	$\sim$	$\sim$	$\checkmark$

Table 1: Comparison of egocentric gaze prediction paradigms.

\mathcal{V}

is the input video and

\mathcal{V}_{\leq t}

its causal prefix,

\mathbf{g}_{t}\in\mathbb{R}^{2}

is the gaze position at frame

t

\mathbf{g}_{1:T}

the full trajectory, and

q_{\theta}(\,\cdot\,\mid\,\cdot\,)

denotes a model’s parametric conditional distribution. MSE: mean squared error, KL: Kullback–Leibler divergence, and BCE: binary cross-entropy.

\checkmark

if satisfied,

\times

if not satisfied, and

\sim

if architecturally implied but not enforced.

Figure. 2: GazeFlow architecture. (a) Dual-path condition extraction (Section 3.1 ). A LoRA-adapted V-JEPA 2 backbone produces bottom-up visual tokens $\mathbf{c}_{v}$ ; spatial–temporal pooling and learnable queries form the top-down task-token bank $\mathbf{c}_{\text{task}}$ . (b) Conditional velocity network (Section 3.2 ). Latent gaze tokens encoding $\mathbf{x}_{s}$ and $s$ pass through six DiT blocks, each containing four sub-layers; a linear head outputs the velocity field $\mathbf{v}_{\theta}$ . (c) Flow-matching sampling dynamics. Euler integration of $\mathbf{v}_{\theta}$ transports Gaussian noise $\mathbf{x}_{0}$ to a clean trajectory $\mathbf{x}_{1}$ ; the cyan sample cloud at $s\!\in\!\{0,0.2,0.4,0.8,1\}$ contracts toward the ground truth.

		EGTEA Gaze+					Ego4D
Paradigm	Method	AUC $\uparrow$	F1 $\uparrow$	P $\uparrow$	R $\uparrow$	AAE $\downarrow$	AUC $\uparrow$	F1 $\uparrow$	P $\uparrow$	R $\uparrow$	AAE $\downarrow$
Simple Priors	Center Bias	0.906	0.185	0.162	0.215	16.07	0.930	0.222	0.194	0.258	13.92
Simple Priors	Random Walk	0.824	0.147	0.090	0.413	16.34	0.763	0.109	0.059	0.662	14.38
Marginal density	GLC [ 26 ]	0.953	0.421	0.348	0.531	10.21	0.947	0.355	0.267	0.530	11.52
Autoregressive	EgoM2P (zero-shot) [ 31 ]	0.921	0.280	0.195	0.498	13.18	0.911	0.253	0.180	0.422	13.86
	EgoM2P (LoRA) [ 31 ]	0.921	0.293	0.205	0.515	12.81	0.930	0.297	0.217	0.470	12.69

Table 2: Per-frame accuracy on EGTEA Gaze+ and Ego4D. GazeFlow achieves the best performance; results on more metrics are in Table 9 in Appendix F.1 . Best per column is in bold.

		ADE ( $\times 10^{2}$ px) $\downarrow$		DTW ( $\times 10^{2}$ px) $\downarrow$		Disp Mean	Disp Med	JSD $\downarrow$
Paradigm	Method	Mean	Best	Mean	Best	( H : $12.5$ px)	( H : $4.0$ px)
Simple Priors	Center Bias	1.35	1.35	1.35	1.35	$0.00\times$	$0.00\times$	$0.125$
Simple Priors	Random Walk	2.07	1.05	1.97	0.92	$2.54\times$	$7.43\times$	$0.294$
Marginal density	GLC [ 26 ]	1.13	1.01	1.03	0.87	$5.77\times$	$11.75\times$	$0.352$
Autoregressive	EgoM2P (zero-shot) [ 31 ]	1.47	0.97	1.35	0.83	$2.20\times$	$3.05\times$	$0.058$
	EgoM2P (LoRA) [ 31 ]	1.50	0.96	1.37	0.81	$2.69\times$	$3.03\times$	$0.058$

Table 3: Alignment with human gaze dynamics on EGTEA Gaze+. Disp Mean and Disp Med are per-step gaze displacements shown as ratios to the human trajectory (subheader H , measured in pixels; closer to

1\times

is better). Best per column in bold.

Table 6

Variant	AUC $\uparrow$	F1 $\uparrow$	P $\uparrow$	R $\uparrow$	AAE $\downarrow$	ADE $\downarrow$	DTW $\downarrow$	Disp Mean	Disp Med	JSD $\downarrow$
								(H: $12.5$ px)	(H: $4.0$ px)
Full	0.970	0.493	0.401	0.638	8.81	0.57	0.45	$\mathbf{0.91\times}$	$\mathbf{1.08\times}$	0.0011
- Bottom-up Visual Feature	0.961	0.431	0.333	0.613	9.22	0.65	0.53	$0.89\times$	$1.17\times$	0.0012
- Top-down Task Guidance	0.965	0.476	0.380	0.638	9.14	0.58	0.46	$0.92\times$	$1.09\times$	0.0013
- Temporal RoPE	0.968	0.477	0.388	0.620	8.94	0.59	0.46	$1.21\times$	$1.40\times$	0.0027
- Joint Distribution Modeling	0.903	0.451	0.394	0.526	9.12	0.84	0.79	$0.71\times$	$2.01\times$	0.0729

Table 6: Ablation study. Full is the default GazeFlow ; each remaining row ablates one architectural component while keeping all others identical, removing either the bottom-up visual feature (

\mathbf{c}_{v}

, frame-local visual cross-attention), the top-down task guidance (

\mathbf{c}_{\text{task}}

, task cross-attention), the relative-time RoPE in time-aware self-attention, or the joint-distribution flow-matching head (replaced with a deterministic regression head over the same conditioning). Per-column best in bold .

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Figure. 3: Per-head patterns of the time-aware self-attention aggregated across the EGTEA Gaze+ validation set. Rows index denoiser layers, columns index attention heads. Each cell shows the canonical attention pattern (query frame on the vertical axis, source frame on the horizontal, both normalized to clip-relative position); brighter pixels correspond to higher attention weight after row-wise softmax. The dashed diagonal marks $\textit{source}=\textit{query}$ , separating two halves with opposite temporal meaning: mass in the lower triangle ( $\textit{source}<\textit{query}$ ) is attention to past frames (look-back), mass in the upper triangle ( $\textit{source}>\textit{query}$ ) is attention to future frames (look-ahead), and mass on both sides marks split-attention heads with peaks straddling the current frame. Cell-frame color encodes this role: look-back (blue), look-ahead (red), split-attention (purple), local (grey).

Figure. 4: Per-head time-aware self-attention as a function of temporal offset. Same heads as Fig. 3 , summarized as a 1-D curve by averaging the attention pattern along each diagonal. The horizontal axis is the offset between source and query frame (negative = past, positive = future); the vertical dashed line marks zero offset. Look-ahead heads peak right of zero, look-back heads peak left, and split heads exhibit two peaks straddling zero with a clear valley at the current frame.

Figure. 5: Hand vs. active-object attention mass per (layer, head) on the EGTEA Gaze+ validation set. Each marker is one of the

48

visual-cross-attention heads, plotted by mean attention mass on the hand (x-axis) and active-object (y-axis) regions; markers are colored by layer, error bars are clip-wise standard deviation. Dotted lines mark the global means (hand

=4.5\%

, object

=22.6\%

). Heads in the lower-right quadrant focus on the hand, those in the upper-left on the active object, and those in the upper-right couple both.

Figure. 6: Layer-wise hand / active-object / background attention mass conditioned on verb. For each of the six most frequent EGTEA verbs we average the per-region attention mass across heads at each layer. Manipulation verbs (“cut”, “put”, “take”, “turn”) keep

25

–

50\%

of the mass on the active object at every layer, while “open” and “close” route most mass to the background regardless of layer.

Figure. 7: Failure cases. Three rows from two validation clips, each row showing $8$ evenly-spaced frames along time. Cyan dots are the $K{=}50$ GazeFlow predictions at that frame; the filled red circle is the recorded ground-truth gaze when it lies within the frame, and the hollow red ring with a yellow halo flags frames where the recording is clamped to the image boundary (off-frame). When the wearer saccades to a target outside the camera’s view, the predicted cloud stays on the visible scene while the ground-truth marker drifts to the boundary.

Backbone	Resolution	Layer	AUC $\uparrow$	F1 $\uparrow$	P $\uparrow$	R $\uparrow$	AAE $\downarrow$
V-JEPA 2 ViT-L	$\mathbf{256{\times}256}$ (native)	last	0.944	0.378	0.292	0.535	11.39
V-JEPA 2 ViT-L	$240{\times}320$ (matched)	last	0.913	0.296	0.216	0.469	12.87
Qwen3-VL-8B	native	2	0.944	0.370	0.280	0.549	11.37
Qwen3-VL-8B	native	4	0.944	0.370	0.280	0.547	11.37
Qwen3-VL-8B	native	8	0.943	0.369	0.278	0.549	11.34
Qwen3-VL-8B	native	16	0.942	0.368	0.279	0.541	11.24

Appendix

Table 7: Linear probing of frozen backbone features on EGTEA Gaze+ . Top block: V-JEPA 2 ViT-L at its native pre-training resolution and at a 4:3 input that matches the dataset aspect ratio. Bottom block: Qwen3-VL-8B at four LM layers, native resolution. All probes are ridge regressions on the frozen spatial token grid; per-column best in bold.

Dataset	Resolution	fps	Train clips	Val clips
EGTEA Gaze+	$640\times 480$	24	8,299	2,022
Ego4D (Aria gaze)	$1088\times 1080$	30	12,178	5,202

Appendix

Table 8: Dataset summary.

Prior-only reference
	EGTEA Gaze+									Ego4D
	Saliency					Localization				Saliency					Localization
Method	NSS $\uparrow$	AUC $\uparrow$	CC $\uparrow$	KL $\downarrow$	SIM $\uparrow$	F1 $\uparrow$	P $\uparrow$	R $\uparrow$	AAE $\downarrow$	NSS $\uparrow$	AUC $\uparrow$	CC $\uparrow$	KL $\downarrow$	SIM $\uparrow$	F1 $\uparrow$	P $\uparrow$	R $\uparrow$	AAE $\downarrow$
Center Bias	0.679	0.906	0.095	6.635	0.110	0.185	0.162	0.215	16.07	0.780	0.930	0.122	5.873	0.136	0.222	0.194	0.258	13.92
Random Walk	0.623	0.824	0.096	3.926	0.101	0.147	0.090	0.413	16.34	0.113	0.763	0.017	4.256	0.072	0.109	0.059	0.662	14.38
Marginal density estimation

Appendix

Table 9: Full saliency and localization comparison on EGTEA Gaze+ and Ego4D.

	F1 $\uparrow$					Time (s) $\downarrow$
$K$ \ $S$	1	2	5	10	50	1	2	5	10	50
1	0.453	0.465	0.444	0.427	0.405	0.49	0.49	0.51	0.54	0.77
10	0.460	0.481	0.484	0.479	0.468	0.52	0.53	0.57	0.63	1.14
30	0.460	0.484	0.493	0.492	0.486	0.58	0.62	0.73	0.91	2.35
50	0.460	0.485	0.495	0.495	0.491	0.65	0.71	0.89	1.21	3.37

Appendix

Table 10: F1 and inference time over the sample count

K

and the Euler step count

S

(EGTEA Gaze+, GazeFlow LoRA). Time is per

64

-frame clip on a single H100. The recommended setting is in bold.

Method	ms / frame $\downarrow$	Throughput (fps) $\uparrow$
GLC [ 26 ]	12.5	80
AT [ 17 ]	219	4.6
EgoM2P [ 31 ]	506	2.0
GazeFlow ( $K{=}50$ , $S{=}50$ )	53	19
GazeFlow ( $K{=}30$ , $S{=}5$ )	11.4	88

Appendix

Table 11: Inference time across methods on a single H100. The ms / frame column gives the time to predict gaze for one video frame, and throughput is its inverse.

						ADE ( $\times 10^{2}$ px) $\downarrow$		DTW ( $\times 10^{2}$ px) $\downarrow$
$K$	AUC $\uparrow$	F1 $\uparrow$	P $\uparrow$	R $\uparrow$	AAE $\downarrow$	Mean	Best	Mean	Best
1	0.866	0.398	0.350	0.462	11.10	1.05	1.05	0.96	0.96
5	0.942	0.457	0.378	0.578	9.61	1.05	0.80	0.95	0.68
10	0.953	0.469	0.395	0.579	9.28	1.05	0.73	0.95	0.60
30	0.961	0.487	0.399	0.623	9.05	1.04	0.64	0.95	0.51
50	0.964	0.491	0.414	0.603	9.01	1.04	0.60	0.95	0.48

Appendix

Table 12: Sample count

K

sweep on EGTEA Gaze+. ADE and DTW are reported in

\times 10^{2}

px following Table 3 ; “Mean” and “Best” are mean-over-

K

and best-of-

K

. At

K{=}1

the two coincide. The

K{=}50

row matches Table 2 and Table 3 .

$M$	AUC $\uparrow$	F1 $\uparrow$	P $\uparrow$	R $\uparrow$	AAE $\downarrow$
1	0.968	0.492	0.404	0.630	8.68
2	0.967	0.486	0.392	0.639	8.87
4	0.970	0.493	0.401	0.638	8.81
8	0.968	0.483	0.390	0.635	8.79
16	0.968	0.480	0.381	0.650	8.75

Appendix

Table 13: Capacity sweep over learnable task queries.

M

is the number of learnable task queries, and every variant keeps the global pool token. Per-column best in bold.

Table 20

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EyeTAG: Eye Trajectory-Aware Gaze Estimation

Jungmin Lee, Niamat Ullah, Yoseob Han

Department of Information and Telecommunication Engineering Soongsil University Seoul, Republic of Korea · Department of Electronic Engineering Soongsil University Seoul, Republic of Korea

Gaze Target Estimation

Open paper AskPDF arXiv

Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict each frame independently, so consecutive outputs fluctuate as jitter. Multi-frame methods reduce this, but they learn motion implicitly inside appearance features, so the gaze trajectory is never an explicit variable. We propose EyeTAG (Eye Trajectory-Aware Gaze Estimation), a causal multi-frame framework built around an explicit first-order gaze prior: at each step it differentiates its own recent predictions and feeds the resulting trajectory back as a compact kinematic token. Because differencing is translation-invariant in gaze space, this token carries subject-invariant motion rather than personal gaze offsets. Face and eye streams supply visual evidence, fused by cross-attention and a causal Transformer decoder. EyeTAG reduces the mean angular error by about 1.0

^\circ

on Gaze360 and performs on par with the strongest baseline on EVE (2.56

^\circ

vs. 2.58

^\circ

). Within-model ablations, which keep the encoder and the rest of the architecture fixed and vary only the gaze history, show that the differential formulation, rather than temporal context alone, removes the systematic saccade bias that persists even with an absolute gaze-history prior. Our code is available at https://github.com/peter8366/EyeTAG.

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