Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Tracks
Authors: Dmitrii Pozdeev
Alexey Artemov
Ananta R. Bhattarai
Artem Sevastopolsky
Venue: ICLR 2026 Poster (2026)
Keywords: Human heads
3D shape correspondence
foundation models
vision transformer
point tracking
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Dense representation of human head images learned from in-the-wild image collections and point tracks, greatly improving monocular tracking and other applications.

Abstract: We propose DenseMarks -- a new learned representation for human heads, enabling high-quality dense correspondences of human head images. For a 2D image of a human head, a Vision Transformer network predicts a 3D embedding for each pixel, which corresponds to a location in a 3D canonical unit cube.  In order to train our network, we collect a dataset of pairwise point matches, estimated by a state-of-the-art point tracker over a collection of diverse in-the-wild talking heads videos, and guide the mapping via a contrastive loss, encouraging matched points to have close embeddings. We further employ multi-task learning with face landmarks and segmentation constraints, as well as imposing spatial continuity of embeddings through latent cube features, which results in an interpretable and queryable canonical space. The representation can be used for finding common semantic parts, face/head tracking, and stereo reconstruction. Due to the strong supervision, our method is robust to pose variations and covers the entire head, including hair. Additionally, the canonical space bottleneck makes sure the obtained representations are consistent across diverse poses and individuals. We demonstrate state-of-the-art results in geometry-aware point matching and monocular head tracking with 3D Morphable Models. The code and the model checkpoint will be made available to the public.

Published as a conference paper at ICLR 2026
DENSEMARKS: LEARNING CANONICAL EMBEDDINGS FOR HUMAN HEADS IMAGES VIA POINT TRACKS
Dmitrii Pozdeev', Alexey Artemov Ananta R. Bhattaraaa, Artem Sevastopolsky'
Technical University of Munich (TUM) University of Bielefeld
Ras
Video frame
Tracked head mesh
left byebrow
right ear
CP nose
Monocular head tracker
mouth
Marks space
Monocular head tacker + DenseMarks
Figure 1: Our method learns to embed a human head image into a semantics- aware volumetric representation based on a large collection of in-the-wild talking head videos annotated by an off- the-shelf point tracker (left). The embeddings can be estimated in feedforward way and used for downstream applications, such as monocular tracking (right), stereo reconstruction, and many others.
ABSTRACT
We propose DenseMarks abling high-quality dense correspondences of human head images. For a 2D) im- age of human head, Vision Transformer network predicts 3D embedding for each pixel, which corresponds to a location in a 3D canonical unit cube. In order train our network, we collect dataset of pairwise point matches, estimated by a state-of-the-art point tracker over collection of diverse in-the-wild talking heads videos, and guide the mapping via a contrastive loss, encouraging matched points to have close embeddings. We further employ multi-task learning with face landmarks and segmentation constraints, as well imposing spatial continuity of embeddings through latent cube features, which results in an interpretable and queryable canonical space. The representation can be used for finding common semantic parts, face/head tracking, and stereo reconstruction. Due to the strong supervision, our method is robust to pose variations and covers the entire head, in- cluding hair. Additionally, the canonical space bottleneck makes sure the obtained representations are consistent across diverse poses and individuals. We demon- strate state-of-the-art results in geometry-aware point matching and monocular head tracking with 3D Morphable Models. The code and the model checkpoint will be made available to the public.
learned representation for human heads,
INTRODUCTION
Modern applications in augmented and virtual reality (AR/VR), telecommunications, computer gaming, and movie production require building models of humans at increasingly demanding level of quality. Flaws in face and head modeling are particularly noticeable to human users (Haxby et al., 2000 Blanz & Vetter 1999). most human head modeling pipelines rely on head track- ing (Thies et 2016 Giebenhain et al., 20244 Qian et al.] 2024) to identify and maintain cor- respondences between head feature locations. Existing methods excel at features with consistent

---

Published as a conference paper at ICLR 2026
statistical regularities across subjects. For instance. sparse facial landmark tracking (ugaresi al. 2019a; Cao et al., 2019) aims to follow the locations of unambiguous but isolated facial features shared by typical faces, such as the outlines of the eyes, nose line, or mouth corners. Similarly, para- metric 3D model estimation and tracking (Blanz & Vetter 1999; Li et al., 2017b) Dai et al. 2020) assumes that most face and head geometries follow statistical shape model and can be represented by a shared, comparatively simple mesh template.
However, features like hair, accessories, and clothing are often omitted from head tracking, which typically focuses on landmarks or skin. In a typical video capture, individual landmarks or entire head regions easily become occluded due an extreme pose, expression, or worn accessory, introducing large errors in tracking. As result, head tracks produced by conventional approaches are fundamentally limited by their incompleteness and correspondence instability.
To improve robustness of correspondence search, one path forward is to extract and match repre- sentations densely in each image pixel instead of detection and alignment of isolated landmarks. Recent image-based vision foundational models (VFMs) are one suitable source of such dense rep- resentations known to be effective in many vision tasks (Dutt et al. 2024, Siméoni et al., 2025). As human heads constitute visual category with high structural similarity across instances, it is natural to expect such representations defined at unambiguous facial features to be nearly view- and time-invariant, facilitating exact correspondence search.
Building on these insights, we propose DenseMarks, a new learned representation for human heads designed to (1) enable high-quality dense correspondences for complete human heads, including ir- regular features such as hair or accessories, (2) achieve robust tracking under challenging conditions such strong occlusions, and (3) produce structured, interpretable, and smooth canonical latent space for exploration and interaction. We use a ViT neural backbone to predict dense per-pixel repre- sentations within the head mask of an input image; leveraging powerful pre-trained VFMs (Siméoni et al. 2025). These representations are projected into shared 3D space, reducing correspondence to nearest-neighbor search and enabling intuitive interactions (e.g., click-based retrieval). To train without ground-truth dense correspondences, we construct a diverse dataset of human head videos with 2D point tracks from an off-the-shelf tracker (Karaev et al., 2024a). We enforce fine-graines cross-subject consistency by optimizing contrastive loss on matched pairs, and integrate semantic and smoothness constraints to structure the latent space and improve interpretability.
We benchmark against pre-trained VFM variants (Siméoni et al.
2025 Khirodkar et al. 2024 Yue
ct al. 2024), , with assessment focused on dense image warping and geometric consistency measures.
RELATED WORK
Face, Head, and Full Body Tracking. Commonly, tracking humans videos involves extracting relevant information for the estimation and alignment of their pose and shape. In the simplest form, this is achieved by predicting locations of characteristic landmark points with fixed semantics (Sag-)
onas al. 2013 Moon et al. 2020 Jin al. 2020) using learned models (Bulat & Tzimiropoulos 2017 Lugaresi et al.] [201 9a] Cao et al. 2019 Simon et al., 2017]] [i et al.) 2022). Hase of collecting
annotations and efficiency of landmark detectors have made landmarks essential in practical tracker
design, enabling initial rigid alignment (Qian] 20224] Qian et al. 2024 Grassal et al. 2021 Bogo
al. 2016 Kanazawa et al., 2018 Kocabas et al. 2020). However, relying on a finite number of isolated, sparse landmarks can compromise robustness, commonly requiring regularization or
postprocessing such as temporal smoothing (Qian 2024 Zielonka et al. 2022 Zheng et al.) 2023a
Huang et al. 2022 Jiang et al. 2022).
Many methods for estimating and tracking parametric models of faces and bodies (3DMMs Blanz & 2017 Li et al. 2017b] Zhang et al., (202bb Romero et al.. 2017 Loper et al.) 2015 Dai et al. )020) are based on the sawllysii-by-yynthee
Vetter |999| et al.
paradigm (Blanz & Vetter 1999 Zhu
ct al. 2017 Feng et al. 2021 Zielonka et al.) [022] Danéček et al. 2022) that involves a combination of rigid alignment and optimization of denser losses. While offering higher geometric completeness, such models rely on simple mesh topology and limited range of geometries captured by PCA basis (Abdi & Williams Dollffe 2011); for fitting. they commonly depend on prior landmarks estimation and optimize highly non-convex (e.g., photometric or depth) losses.
2

---

Published as a conference paper at ICLR 2026
Our method naturally complements 3DMM -bssedd head trackers by supplying dense, robust semantic correspondences for complete heads and includes features not trivially captured by landmarks or parametrie models (e.g., hair). This idea is similar to works that learn to predict texture coordinates for alignment of parametric face (Feng et al.] 2018 Giehenhain et al. 2025) and body (Güler et al.)
2018 lanina al.
2022) models, or compute multi-dimensional features, normals, and depth using foundation models optimized for the human domain (Khirodkar et al. 2024).
Canonical Space Learning. Our method represents input samples by learned embeddings in shared (canonical) space. The idea of using canonical representations for category-level object local- ization and pose estimation was pioneered by Normalized Object Coordinate Space (NOCS) (Wang) et al., (219) and subsequently extended to handle sparse views. lack of dense labels, or multiple cat- egories (Min et al., 2023 Xu et al. 2024. Krishnan et al., 2024). J. However, directly learning NOCS representations for 3D heads is diffiuut as large collections of 3D models are absent in the human head domain.
Shape correspondence task can be formulated as a problem of finding mapping between spaces of functions defined on shapes (Ovsjanikov et al., 2012 Rodolà et al.) 2017). Existing methods applying such functional maps for finding full-body correspondences (Neverova et al. 2 [anina jet al. 2022) require fitting parametric 3D models for supervision. To enable modeling parts of human heads absent from parametric models, opted not to use these in training.
The idea of using canonical space is widespread in 3D-aware per-scene human fitting (Gaini et al.
2021
Park et al., (021) and human generative modeling EG3D (Chan al., 2022. Dong et al.
2023 Similarly, several works focus on producing unsupervised shape correspondences, in part
based on functional maps (Halimi et al. 2019 Cao & Bermard 2022
Cao et al. 2023 Liu al.
2025).
Embeddings from Foundation Models. Recent progress in ViT-based VFMs CCarn et al. 2021
Oquab et al. 2023 Siméoni et al., 2025 Weinzaepfel et al. 2022; Dosovitskiy et al. [0200] Han
al. 2022) and evidence of their emerging understanding of 3D world (Zhang al. 2024b] Sucar
ct al. 2025 Chen et al.] 2025a) has fueled efforts to improve their 3D-awareness through fine-
tuning Yue et al.] 20024] Zhang et al., 2024a). Similarly, directly training siamese VIT networks on pairs of stereo views has been shown to efficiently establish dense correspondences (Wang et al.)
2024
Leroy et al. 2024 Smart et al. 2024; Chen et al. 2025b), when prompted with 2+ images.
anotherclasse of VFMs, pre-trained diffusion models (e.g., Stable Diffusion (Rombach et al.) 2021
allow inferring semantic correspondences from their image-hased representations (Hedlin et al, 2023 Zhang et al. 2023a Zhu et al.) (024) that could be distilled into dense surface correspon- dences across objects of arbitrary categories (Dutt et al. 2024). In our experiments, we found the correspondences arising from point tracking (cf. next paragraph) more reliable than those arising from pretrained diffusion models. Our method benefits from integrating VFMs as a feature extractor; in contrast to generic pre- mreitrined deep features correlated with visual semantics, our geometry- aware representations yield an interpretable 3D canonical space.
Point Tracking. The advent of talking heads datasets (Wang et al. 2021 Zhu al. 2022 Ephrat et al. (2018) and point trackers calls for approaches to tracking faces and bodies, free of an underly- ing coarse parametric model. In particular, in line of works starting from PIPs (Harley al. (022))
deep learning based methods are proposed to track any queried point along the video. Progress in the area of point trackers has been additionally accelerated by the appearance of suitable bench- marks, such as Tap- Vid (Doersch et al.) 2022) and PointOdyssey (Zheng et al.) (023b). A series of consequent improvements of mack-any--oint algorithms (Doersch et al. 2003 Li et al. 2024
Cho et al. 2024) led to the emerging branch of CoTracker works (Karaev et al. 2024b a). as well as Boots TAP (Doersch et al. 2024). Similarly, few methods rely on foundation models, such as (Tumanyan et al. (024)) for tracking any point VGGT (Wang al. 2025) that uses point tracks for 3D understanding. Applications of modern algorithmic ideas for point tracking also led to the appearance of simultaneous reconstruction and tracking methods such as Dynamic 3D Gaussians (Luiten et al. 2024). St4rTrack (Feng et al. (005). or Tracks-to-4D (Kasten et al.) 2024). For the downstream tasks of human tracking, similar to our method, some of the recent approaches also make use of point tracking (Kim et al. 2025 Taubner et al. 2024) or motion data (Shin et al.)
DINO-
2024
3

---

Published as a conference paper at ICLR 2026
Begio
Latent Seatures
Tolmm
Emmbedeer tee
Image Jj Xx
Embeddings
Semcatic festures Fost
shared weights
1/2 E Rxxx
Cube locations
Semantic Soatures Fous?
Fibeeddr Oss
Reginn
Embeddings E "xW
Image Ja RxWWx
Cube locations
Figure 2: learn our representation, we train an embedder network do in a siamese fashion. By feeding two image frames from talking head video of the same person into the embedder inde- pendently, we obtain DenseMarks embeddings L/. 12. These embeddings correspond to canonical locations in the unit cube (DenseMarks space). This cube is discretized in advance, and a learn- able matrix of latent features represents D-dimensional vectors, storing semantic info of each of the voxel grid locations. To transform each of the estimated cube locations into semantic features Feat', Fe we query E at locations 13. 13 via trilinear interpolation (TriLerp). For the images 1, 12, have
of pair matches K1 K2 estimated by an off-the-shelf point tracker (Karaev et al. (2024)). We apply contrastive loss (Radford et al. 2021) to the semantic features of images in these locations. This way, the cube locations corresponding the same semantic feature are pushed closer together. Additionally, we estimate region masks S S2 by semantic network Se and apply segmentation loss.
METHOD
In this section, we define the representation (section 3.1) and the way [2D image embeddings] estimator is trained (section 3.2) The method overview is illustrated in Figure
3.1 DENSEMARKS SPACE
The architecture of our pipeline consists two key components: the canonical space where the embeddings reside, and the embedder, the task of which is to map image into this space. The requirements that we set for the space are: (1) interpretable and queryable (the user can query a point in the space by looking at typical arrangement of regions in it); (2) structured (regions meaningful and don't i overlap); (3) complete (contains the whole head, including the parts that are not trivial to annotate, such hair and accessories); (4) smooth and continuous (images will be mapped to continuous manifold of the space, with regions not getting abrupt and intersecting each other).
Additionally, we know that human heads are 3D objects. Even though UV (i.e., 2D canonical space) a typical surface representation for heads, it's not the most precise representation due modeling a complete head, including, e.g., hair and accessories, being not trivial in UV space and featuring seams (lanina al. 2022). Because of this, we decide to represent our canonical space unit cube in 3D and make the canonical embeddings the locations in this cube.
The interpretability requirement (1) and structure requirement (2) are enforced via landmark and segmentation losses, defined further in section 3.2
The completeness requirement (3) is enforced with the way the embedder is supervised (also see sec- tion 3.2). For that purpose, we add latent grid on top of the cube of a given resolution N₂ x N₂ x Na and attach a D-dimensional latent feature each element of the voxel grid, thus forming learnable matrix En e (N,) Each latent feature contains a highly highly-dimensional info about the given location in the canonical space.

---

Published as a conference paper at ICLR 2026
Finally, to promote the smoothness requirement (4), apply spatial smoothness to the matrix Enw via a 3D Gaussian filter with a strength of a. thus creating a latent feature grid = gaussian filter (D( σ). This encourages the predicted embeddings from the embedder to be smoother, since the semantics of the close points in the cube will be similar and smoothly changing.
Note that the of matrix E is inspired by the similar matrix of latent features used in functional maps, e.g., in CSE (Neverova et al., 2020b), that is typically smoothed via a Laplace-Bellne Beltrami operator (Lévy 2006). From different standpoint, the operation of querying the space can also be seen as an attention operation, where the locations are queries (same as keys in this context) and the latent grid features are values. By aggregating the values real-valued query locations with trilinear interpolation weights, we obtain the resulting semantic features at given location.
3.2 EMBEDDER TRAINING
Our goal is to learn a monocular embedder dhe I Ic. where E R is an input RGB image and Ic E I 1Rxxxx3 the predicted canonical embeddings for each pixel.
The network consists of Vision Transformer backbone that predicts a feature map, which is further gradually upscaled through a sequence of convolutional layers to match the input resolution.
To train this network, at each training step, we pass two input images 1 E IR "xxx through the (I2) both I x3 For these two images, we assume having number of ground truth pixel correspondences be- jb)}; (f) (z)): These correspon-
embedder the and obtain corresponding predictions Igg +,))
tween them ( (Kg. K2) = (((..))).. dences could be coming from any off-the-shelf pairwise matching algorithm. In our case, we obtain them from a point tracker inferred over individual talking head videos, as we found best in prac- tice. Because of this, in our training procedure, images /1 and l2 are always coming from the same talking head video, but can represent arbitrarily close or far frames of the same video.
real-valued locations in the canon- ical space. For each of those, we extract their corresponding D-dimensional semantic features via trilinear interpolation (Trilerp) (Bourke 1999 13 E IR. HxDxD 1 1eat E RHxWxD where
Embeddings It- (I2) and I2 (Ig) point
(Teadis = Trilerp(F) F. (Tg) (Pen)) Tnlerpp (T))
In order to supervise our network, we encourage the features Pa 1 to be close at the posi- tions, defined by ground truth correspondences (K1 K2), and far for other pairs of points. More formally, we first extract semantic features at the integer spatial positions of the ground truth cor- respondences, yielding tensors of queried features Feat Feat² E R Feat, Ica (K)))) Feat, = (imml(())p)) To promote the corresponding features of the first and second image to be close (positive pairs) and the others to be far (negative pairs), construct contrastive loss similar to CLIP Loss (Radford et al., 2021 that requires the pairwise matrix of cosine distances to close identity matrix:
Coente (Feat", Feat²) = [| (norm((aa²)) -111
where norm is row-wise normalization operation.
Additionally, we apply number of regularizations. To reduce ambiguity of the learned canon- ical space, we impose the locations of standard 300W [Sagonas al.) (2013) format face land- marks to be close to the predefined locations in the cube. This is implemented via inferring an off-the-shelf landmark predictor on images I,, I2, thus obtaining ground truth landmark locations
(H..... lgs), (f.....,(gs) and anchoring them to the predefined locations LA E RR, k 1,=1,... I...., 68 in the unit cube:
Imkk (Te (2)
Σ k=1
[T[la]] Lx.
To further correlate the predicted canonical embeddings with image semantics, add trainable segmentation head Sg, consisting of a single convixl layer. For each of the images, this head receives the extracted semantic features (either Feat or Feat?) and returns the predicted logits of probabilities of class regions (face parsing) either S Se (Feat"), S Se Se both in R xxxWxNs The segmentation loss expression compares each of the predicted masks S ( S2)
5

---

Published as a conference paper at ICLR 2026
to the corresponding ground truth mask Sgt E R obtained by off-the- face parser:
p (S (ga) Σ
cross pross.entropy(Sli.. Sglt,1))
W
The overall loss is as follows:
(.) = L (Feat Featt)
+ Aimka (1o (Io ( )- + 10 1l (12 (?))
(1)
+ Asegm (1rrm (S² ((a) 1 pogm(S$ |
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Data. We train our method on CelebV-HQ dataset (Zhu et al. 2022) of 35K in-the-wild talking
head videos of interview style. To obtain ground truth correspondences (K1, 211 K2), we run Co-
Tracker3 (Karaev et al. 2024a) on these videos. As an input set of points to track, we take the whole foreground region of the first frame (estimated by AroundedSAM2 (Ren et al. 2024) prompted with the text "person ") and sample points uniformly in that region (see an example in Fig. | (left)). Videos were discarded if there were either too few tracks found (fewer than 80) or foreground seg- mentation failed, resulting in 32K videos left. The number of point tracks found did not exceed 400. We show samples of point tracks visualisation in the appendix 100 randomly sampled videos have been held out for the evaluation and used in the results described below. Each training batch is formed by uniformly sampling two random frames from a sample video from the constructed anno- tated dataset. All videos resized to the (512, 512) resolution in advance and fed to the embedder in that resolution. For augmentation, we use random shift (in [-10%, 10%] range), scale ([-10%, 10%)), and rotation ([-18,, 18 each with chance of 50%. Points which are longer visible af- ter augmentation are no longer accounted in training. For the landmark loss, we extract 70 manually selected landmarks (full face border, landmarks on eyes, nose, and mouth) via Mediapipe (Lugaresi)
iet al., (019b). Ground truth segmentation masks are obtained via FaRL (Zheng et al. 2022bb and
Turther refined on the borders via face-parsing (Jonathan Dinu 2025 Xie et al. 202 which
works better in practice on non-face regions of the head.
Architecture and training. To make use of strong pretraining, we initialize the embedder with pre-trained DINOv3 (Siméoni et al.) (205)) checkpoint and add DPT head (Ranful et al. 2021) to output an image of the same spatial resolution as the input (512 x 512). Matrix is initialized from Gaussian distribution N 1). We use Asegm 1 for the segmentation loss and Atmks 50 for the landmark loss. For optimization, we employ the Adam (Loshchilov 2017) optimizer with a learning rate 5 10 for the backbone of dg. learning rate of 10- for DPT head, and 10-3 3 for the latent features E. The schedule for all learning rates was cosine annealing with an overall number of steps of 140K and warmup for 2'800 steps. Weight decay of 10 was applied to the network parameters 9 and ç, except for normalization layers. The whole pipeline is trained for 140k training steps using 8 pairs of images per batch single NVIDIA RTX 3090 Ti GPU for 1.5 days.
Baselines. We compare against state-of-the-art general-purpose dense feature extractors, the em- beddings of which provide rich semantic information: DINOv3 (Siméoni et al.) 2025) (embedding dimension: 768), Diffusion Hyperfcatures (Luo et al. 2023) (384), Fit3D (uue et al.
(024) (768). Additionally, consider head-centric dense feature extractors: Sapiens KKhiodkarr al. 2024)
(1280), CSE (Neverova et al.) 2020a) (16) and FaRL (Zheng et al. 20226 (768). All of our base- lines predict dense feature embeddings that can be used directly for a neighbor region search. For visualizations, we choose the two strongest baselines from each category. For completeness, we add comparison with 3DMM-related methods in the appendix [
4.2 RESULTS
Point querying. The requirement of the canonical space is that the same semantic points will have a fixed location in the cube, regardless of the person's 's identity. We test this on a number of points that have distinct semantics: points hair, ear centers, forehead center, eyebrow corners.

---

Published as a conference paper at ICLR 2026
Sagiens (1280)
(384)
Sapiens (1280)
(16) DHFecatt (384) (768)
Figure 3:
Point querying. We select specific point few images and find the reference embed- ding by averaging the embeddings predicted by each of the models in its location. Points: red = on the left side of long hair region, green center of the right ear, orange center of the left ear, blue forehead center, yellow = left eyebrow corner. We indicate the embedding dimension in brackets.
Figure 4: Semantic regions head images can be located via selecting corresponding volumetric regions in the canonical space. Red: on the left side of long hair region, blue: forehead center, green and orange: ears, yellow: skin near the left eyebrow corner.
Sapiens
DHiicats
Figure 5: Dense warping. Here, copy pixels from source to target based on the target ssouce nearest neighbors search in the space of embeddings, predicted by each model (even rows). For clarity, mapping of meshgrid-like coordinates, blended with RGB, is shown additionally (odd rows). Even though deep feature extractors provide valuable matches, they are either matching colors, not semantics (Sapiens (Khirodkar et al. 2024). DHFeats (Luo al. 2023)) ), feature significant arti- facts (DINOv3 (Siméoni et al. 2025).
CSE (Yue et al. 2024 ), thus being less reliable for matching.
To find each semantic point, we manually annotated sample images from CelebV-HQ, inferred the trained embedder, and averaged predicted locations in the cube for each annotated point. We use the obtained location as a reference to find the nearest neighbor in the other image among their predicted embeddings. Results demonstrated in Fig. For baselines, semantic points

---

Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison. On same-person pairs of images from Nersemble (Kirschstein) et al. 2023), we evaluate the quality of correspondences that arise from matching nearest neighbor embeddings. Similarly, on cross-person pairs, we evaluate the consistency and identity preservation.
Same-perso
Cross-person
Matching quality
Identity preservation
MAE
RMSE
PCK @r-0.05 1
ArcFace f
Met3R
head- centric Generic
Fit3D Yue al.
2024)
12.75
21.83
0.57
0.236
0.558
Hyperfeatures (Luo
2023
8.26
13.29
0.72
0.329
0.454
DINOv3 (Siméoni al.
2025
7.60
12.69
0.72
0.266
0,460
FaRL (Zheng 2022
21.38
34.41
0.46
0.166
0.632
Sapiens Khirodkar al.
2024)
14.88
24.12
0.56
0.167
0.595
CSE Neverova et al
2020b
11.22
17.92
0.55
0.359
0.490
Ours
3.68
5.90
0.90
0.384
0.388
Video Frame
Head Tracker
Head Tracker Ours
Vidco Frame
Head Tracker
Head Tracker + Ours
Figure 6: Monocular tracking. We evaluate our method on downstream application of applying state-of-the-art off-the-shelf head tracker (Qian) 2024) track 3D Morphable Model template (FLAME (Li et al. 2017a)) over monocular video. By default, this tracker relies on standard 68 face landmarks and photometric loss. Estimating DenseMarks texture of FLAME and applying an additional photometric loss to match with estimated embeddings greatly improves the robustness of the tracker, especially for extreme poses.
also estimated by averaging predicted embeddings. Despite using significantly smaller vector dimension (3) to store semantics in the embedding, our method can find a corresponding region for challenging views better. Note that our method is also robust strong face or head occlusions.
Region selection. In Fig. we demonstrate how the same volumetric region the canoni- cal space is mapped onto images of people. The regions are initially selected on random im- ages manually and averaged (via a voting pro- cedure) in the cube space.
View
View
View
[3IK pis)
Dense warping. To demonstrate the seman- tie consistency embeddings predicted for the whole image, not only specific points gions, we demonstrate the warping by embed- dings in Fig. 5| evaluated pairs of different
Figure 7: Stereo Reconstruction. We triangulate 2-view and 3-view correspondences of our rep- resentations using known camera parameters in Nersemble (Kirschstein et 2023
8

---

Published as a conference paper at ICLR 2026
Table 2: Importance of the canonical space. On same-person pairs of images from Nersem- ble (Kirschstein et al., 2023), we evaluate the quality of correspondences that arise from matching nearest neighbor embeddings. Similarly, on cross-person pairs, we evaluate the consistency and identity preservation.
MAE +
RMSB
PCK PcK@r=0.05+ f
ArcFace t Met3R J
w/o canonical space
6.35
10.20
0.85
0.348
0.455
Ours
3.68
5.90
0.90
0.384
0.388
people from the Nersemble dataset (Kirschstein et al. 2023). For each target image pixel, replace its color with the color of the nearest neighbor by embedding in the source. We expect the warping to be semantically meaningful and smooth. It is observed that when match nearest neighbors by Diffusion Hyperfeatures and especially Sapiens embeddings, the matches turn out to be based on the color similarity, not the semantic similarity. DINOv3 and CSE appear more semantically meaningful but often feature artifacts, making the correspondences imprecise, as best observed in the mapping rows in the figure. To evaluate the quality of the mapping, we estimate face recognition similarity based on ArcFace (Deng et al., 2019) between the source image and the mapping result, as well the consiseeyt metric Met3R (Asim al. 2025), and show the results in Table [T]
Geometric consistency. To assess qualitatively and quantitatively the precision of the estimated cor- respondences through our embeddings, we repeat the Dense Warping experiment
similar way for the (source, target) pairs of images of the same person, not different people, repeated over various we demonstrate the evaluation the correctness of
people from the Nersemble dataset. In Table [ the estimated correspondences between source and target, averaged over ten people from Nersem- ble. As a source of ground truth correspondences, we estimate a complete head mesh from all 16 cameras via GS2Mesh (Wolf et al. 2024) and sample 1K random mesh vertices. The embeddings are evaluated in the projected locations of these vertices.
Canonical space ablation. To separate the importance of the canonical space from pure rep- resentation learning, we train our model without introducing the canonical space learned by our method. While training on head-specific data improves geometric and semantic consistency relative to DINOv3, our method still outperforms this approach (see Table 2 and App. D)
Design choices ablation. Even though the net- work can learn without introduced constraints on landmark locations in the cube and segmen- tation loss, we demonstrate that finding charac- teristic points and regions becomes more prob- lematic in Fig. [ This is explained by less semantically constrained canonical space. We provide quantitative analysis of Atmks Aaegs different smoothing strategies for Eraw and ef- fect of cube resolution N in App.
Ours w/o link loss
Ours wño seum loss
Ours full
EX
EDE
TO EXP
DBOOK
DDOOOK
DROOK
Robustness to pseudo-GT tools. The qual- ity of DenseMarks depends the robustness of the tools used to generate ground-truth la- bels. Errors in point-tracks arise mainly from (1) missing tracks (most common) and (2) in- accurate track positions. To simulate this, for (1) we randomly retain only a fraction of vis- ible tracks; for (2) we add Gaussian noise with increasing deviation (pixels) to track lo- cations. Results are shown Table Al- though stronger noise degrades accuracy more than track omission, even with severe noise 16 px), the method still outperforms all base- lines. We observe even stronger robustness of method against errors in landmarks and seg- mentation masks (see App.[c. Overall, these results indicate that the pseudo-GT tools provide reli- able supervision and that the learned representations resilient to moderate noise. This suggests
Figure 8: Removing the landmark or segmenta- tion loss makes region finding much less reliable. Blue: forehead center, green and orange: ears, yellow: skin near the left eyebrow corner.

---

Published as a conference paper at ICLR 2026
Table 3: Stability of Densemarks to the errors of the point-trackers. (Left): adding gaussian noise to the tracks with standard deviation o. (Right): keeping only a fraction of the original tracks. Our method remains effective under mild noise. We use 50% of the training steps reduce computa- tional costs.
Method
MAF.
RMSE
ArcFace
Met3R
Method
MAE 4
RMSE
AreFace
Met3R
Keep 10%
4.52
7,30
0.376
0.391
16
6.29
9.97
0.355
0.436
Keep 20%
6.70
0.378
0.389
5.14
8.20
0.361
Keep 40%
3.87
6.18
0.379
0,384
0=0
4.26
6.81
0.382
0,390
Keep 80%
0.381
0.383
3.86
0.383
0.378
Ours (1008)
3,85
6.17
0.388
0.384
Ours (or ()
3,85
6.17
0.388
0.384
DenseMarks may generalize to domains where accurate tracks are difficult to obtain, such as full- body capture highly dynamic non-rigid objects.
off- the-shelf head tracker, VHAP (Qian 2024), which supports estimation of the FLAME parametric head model (Li et al. 2017a). It relies on a standard 300-W set 68 sparse landmarks (Sagonas) et al. 2013) for rigid alignment of the template and optimizes for the shape, pose, and expression parameters of FLAME, through estimating RGB texture the FLAME UV space and applying photometric loss. Even though VHAP excels in multi-view settings, monocular videos remain challenging due to potentially failing landmark detection, occlusions, and extreme viewpoints. To aid the tracker in these situations, we add another photometric loss that based estimating 3-dimensional UV texture of DenseMarks embeddings that is compared to the embeddings pre- dicted by the trained embedder for each video frame independently. We run tracking on in in-the-wild monocular videos with different challenging conditions such as strong/fast head rotation, severe hair/accessories occlusions, very close/far cameras. The results are demonstrated in Figure 6 Our method improves robustness the most in cases of extreme poses and yields better alignment in chal- lenging regions, such as neck and ears. We demonstrate the results of tracking over the complete videos in the Supplementary Video.
Monocular tracking. As an example application of our method, we take
Stereo Reconstruction. In Fig. 7 we demonstrate that triangulating 2+ images can be done purely using embeddings from model, the example of sample from Nersemble with known camera poses and intrinsics. This way, we show the capabilities of [multi-view-]stero and dense estimation.
Other applications. We additionally provide results head pose estimation (App. and other objects (App. E. In App. G we demonstrate robustness to strong lightning change, motion blur, color shifts, and synthetic occlusions.
Limitations. Our method relies on video human collection datasets featuring a wide range of appearances and containing videos of sufficient length and resolution of the head region. While CelebV-HQ is among the most extensive publicly available datasets (35k videos), it primarily con- sists of interview-style / film shooting data. As extremely challenging sequences, such as those featuring backside head views, extreme head rotations, and very rapid motion, are uncommon in this dataset, expect a quality drop DenseMarks on those sequences, especially on the monoc- ular tracking benchmark, which requires most of the per-pixel correspondences to be reliable. We provide failure cases of monocular tracking and dense warping in the Supplementary Video and in the Appendix H
5 CONCLUSION
We propose novel representation for human head images and an embedder for dense estimation. The resulting low-dimensional (3D) embeddings are consistent across views and subjects, enabling reliable matching of challenging regions like hair. Despite their compactness, they outperform high- dimensional features from foundation models in geometry- aware tasks like tracking, while benefit- ing from VFM pretraining. Future work could extend approach to full bodies and other domains, which would be anticipated with the appearance of publicly available high-resolution data collec- tions.
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
Dmitrii Pozdeev grateful to Freunde der TUM e. V. for the financial support to attend the confer- ence. We thankful Visual Computing & Artificial Intelligence lab at TUM for providing the computing resources during Guided Research. We thank Shenhan Qian and Yash Kant for useful discussions. Alexey Artemov and Artem Sevastopolsky currently work Apple. This paper is connected to their work at Apple.
REFERENCES
Hervé Abdi and Lynne Williams. Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4):433-459 2010.
Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images. In Proceedings of the Computer 2025.
Vision and Pattern Recognition Conference, pp.
Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH '99, pp. 187-194, USA, 1999. ACM Press//Addison Wesley Publishing Co. ISBN 0201485605. doi: 10011145/311335.11 1556. httptttss.//bi..rggg/// 111500..11111111 4115533 31111555.11115
Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, and Michael Black. Keep it smpl: Automatic estimation of 3d human pose and shape from single image. In European conference on computer vision, pp. 561-578. Springer, 2016.
Paul Bourke. Interpolation methods. Miscellaneous: projection, modelling, rendering. 1(10), 1999.
Adrian Bulat and Georgios Tzimiropoulos. How far are we from solving the 2d & 3d face align- ment problem? (and a dataset of 230,000 3d facial landmarks). In International Conference on Computer Vision, 2017
Dongliang Cao and Florian Bernard. Unsupervised deep multi-shape matching. In European con- ference computer vision, pp. 55-71. Springer, 2022.
Dongliang Cao, Paul Roetzer, and Florian Bernard. Unsupervised learning of robust spectral shape matching. arXiv preprint arXiv:2304. 14419, 2023.
Z. Cao, G. Hidalgo Martinez, T. Simon, S. Wei, and Y. A. Sheikh. Openpose: Realtime multi multi-person person 2d pose estimation using part affinity fields. IEEE Transactions Pattern Analysis and Machine Intelligence, 2019.
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021.
Eric Chan, Connor Lin, Matthew Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-awant 3d generative adversarial networks. Proceedings of the IEEE/CVE conference on computer vision and pattern recognition, pp. 16123-16133 2022.
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang. Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. ShapeNet: An Information-Riet 3D Model Repository. Technical Report arXiv:1512.03013 [cs.GR], Stanford University - Princeton University - Toyota Technological Institute at Chicago, 2015.
Xingyu Chen, Yue Chen, Yuliang Xiu, Andreas Geiger, and Anpei Chen. Easi3r: Estimating disen- tangled motion from dust3r without training. arXiv preprint arXiv:2503.2439/ 2025a.
Zhuoguang Chen, Minghui Qin, Tianyuan Yuan, Zhe Liu, and Hang Zhao. Long3r: Long sequence streaming 3d reconstruction. arXiv preprint arXiv:2507. 18255, 2025b.

---

Published as a conference paper at ICLR 2026
Seokju Cho, Jiahui Huang. Jisu Nam, Honggyu An, Seungryong Kim, and Joon- Young Lee. Local Joon-Young pair correspondence for point tracking. arXiv preprint arXiv:2407.15400 2024.
Hang Dai, Nick Pears, William Smith, and Christian Duncan. Statistical modeling of craniofacial shape and texture. International Journal of Computer Vision, 128(2):547-571, 2020.
Radek Daněček, Michael J Black, and Timo Bolkart. Emoca: Emotion driven monocular face capture and animation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 20311 1-20322, 2022.
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVE conference on computer vision and pattern recognition, pp. 4690-4699, 2019.
Carl Doersch, Ankush Gupta, Larisa Markeeva, Adria Recasens, Lucas Smaira, Yusuf Aytar, Joao Carreira, Andrew Zisserman, and Yi Yang. Tap-vid: A benchmark for tracking any point a video. Advances in Neural Information Processing Systems, 35:13610-13622 2022.
Carl Doersch, Yi Yang, Mel Vecerik, Dilara Gokay, Ankush Gupta, Yusuf Aytar, Joao Carreira, and Andrew Zisserman. Tapir: Tracking any point with per-frame initialization and temporal refinement. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp- 10061-10072, 2023.
Carl Doersch, Pauline Luc, Yi Yang, Dilara Gokay, Skanda Koppula, Ankush Gupta, Joseph Hey- ward, Ignacio Rocco, Ross Goroshin, Joao Carreira, et al. Bootstap: Bootstrapped training for tracking-any-point. In Proceedings of the Asian Conference on Computer Vision, pp. 3257-3274, 2024.
Zijian Dong, Xu Chen, Jinlong Yang, Michael Black, Otmar Hilliges, and Andreas Geiger. Ag3d: Learning to generate 3d avatars from 2d image collections. In Proceedings of the IEEE/CVF international conference computer vision, pp. 14916-14927, 2023.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition scale. arXiv preprint arXiv:2010. 11929, 2020.
Niladri Shekhar Dutt, Sanjeev Muralikrishnan, and Niloy J Mitra. Diffusion 3d features (diff3l): Decorating untextured shapes with distilled semantic features. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 4494 4504, 2024.
Ariel Ephrat, Inbar Mosseri, Oran Lang, Tali Dekel, Kevin Wilson, Avinatan Hassidim, William T Freeman, and Michael Rubinstein. Looking to listen at the cocktail party: A speaker-independent audio-visual model for speech separation. arXiv preprint arXiv: 1804 03619, 2018
Haiwen Feng, Junyi Zhang, Qianqian Wang. Yufci Ye, Pengcheng Yu, Michael Black, Trevor Darrell, and Angjoo Kanazawa. St4rtrack: Simultaneous 4d reconstruction and tracking in the world. arXiv preprint arXiv:2504.131 13152, 2025.
Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, and Xi Zhou. Joint 3d face reconstruction and dense alignment with position map regression network. In Proceedings of the European confer- ence on computer vision (ECCV), pp. 534 551, 2018.
Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. Learning animatable detailed 3d face model from in-the-wild images. ACM Transactions on Graphics (ToG), 40(4):1-13, 2021.
Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic neural radiance fields for monocular 4d facial avatar reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8649 2021.
Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos, Martin Rünz, Lourdes Agapito, and Matthias Nießner. Mononphm: Dynamic head reconstruction from monocular videos. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024.
12

---

Published as a conference paper at ICLR 2026
Simon Giebenhain, Tobias Kirschstein, Martin Rünz, Lourdes Agapito, and Matthias Nießner. Pixel3dmm: Versatile screen- ssreee priors for single-imag 3d face reconstruction. arXiv preprint arXiv:2505.00615. 2025.
Philip-William William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural head avatars from monocular rgb videos. arXiv preprint arXtv:2112.015544 2021.
Riza Alp Güler, Natalia Neverova, and lasonas Kokkinos. Densepose: Dense human pose estimation in the wild. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 7297-7306, 2018.
Oshri Halimi, Or Litany. Emanuele Rodola. Alex M Bronstein, and Ron Kimmel. Unsupervised learning dense shape correspondence. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pp. 1370-4379, 2019.
Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Ychui Tang, An Xiao, Chunjing Xu, Yixing Xu, al. A survey vision transformer. IEEE transactions pattern analysis and machine intelligence, 45(1):87-118) 2022.
Adam W Harley, Zhaoyuan Fang, and Katerina Fragkiadaki. Particle video revisited: Tracking through occlusions using point trajectories. In European Conference on Computer Vision, pp- 59-75. Springer, 2022.
Adam W Harley, Yang You, Xinglong Sun, Yang Zheng, Nikhil Raghuraman, Yunqi Gu, Sheldon Liang, Wen-Hsuan Chu, Achal Dave. Suya You, et al. Alltracker: Efficient dense point tracking at high resolution. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 52535555550 2025.
James Haxby, Elizabeth A Hoffman, and Ida Gobbini. The distributed human neural system for face perception. Trends in cognitive sciences, ((6):223-233 2000.
Eric Hedlin, Gopal Sharma, Shweta Mahajan, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, and Kwang Moo Yi. Unsupervised semantic correspondence using stable diffusion. Advances in Neural Information Processing Systems, 36:8266-8277 2023.
Thorsten Hempel, Ahmed A. Abdelrahman, and Ayoub Al-Hamadi. 6d rotation representation for unconstrained head pose estimation. 2022 JEEE International Conference on Image Processing (ICIP), pp. 2496 2500, 2022. URL htLhhtts:://aat awt.aapp..mmmattmm wwwantimasseeeeeeeee semanticscholar..........o wwtp/o/rp/rrris.o.ooooopoo
24 0171551033 03
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv: 1:::::7044 1704.04861, 2017.
Yinghao Huang, Omid Taheri, Michael Black, and Dimitrios Tzionas. Intercap: Joint markerless 3d tracking of humans and objects in interaction. In DAGM German Conference on Pattern Recognition, pp. 281-299. Springer, 2022.
Anastasia Ianina, Nikolaos Sarafianos, Yuanlu Xu, Ignacio Rocco, and Tony Tung. Bodymap: Learning full-body dense correspondence map. In Proceedings of the IEEE/CVF conference on computer vision and pallern recognition, pp. 13286-13222 2022.
Jiaxi Jiang, Paul Streli, Huajian Qiu, Andreas Fender, Larissa Laich, Patrick Snape, and Christian Holz. Avatarposer: Articulated full-body pose tracking from sparse motion sensing. In European conference on computer vision, pp. 443-460. Springer, 2022.
Sheng Jin, Lumin Xu, Jin Xu, Can Wang. Wentao Liu, Chen Qian. Wanli Ouyang, and Ping Luo. Whole-body human pose estimation in the wild. In European Conference on Computer Vision, pp. 196-214. Springer, 2020.
Ian Jolliffe. Principal component analysis. In International encyclopedia of statistical science, pp- 1094-1096. Springer, 2011.
13

---

Published as a conference paper at ICLR 2026
Jonathan Dinu. jonathandinu/face--aaaaaa Face parsing model (fine-tuned from segformer celebamask-hgg.. Accessed: 2025-09-25.
co
2025.
Angjoo Kanazawa, Michael J Black, David W Jacobs, and Jitendra Malik. End-to-end recovery of human shape and pose. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7122-7131, 2018.
Nikita Karaev, Iurii Makarov. Jianyuan Wang. Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Cotracker3: Simpler and better point tracking by pseudo-labelling real videos. arXiv preprint arXiv:2410. 1831, 2024a.
Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Cotracker: It is better to track together. In European conference on computer vision, pp. 18-35. Springer, 2024b.
Yoni Kasten, Wuyue Lu, and Haggai Maron. Fast encoder-based 3d from casual videos via point track processing. Advances in Neural Information Processing Systems, 77:96150-96189 2024.
Rawal Khirodkar, Timur Bagautdinov, Julieta Martinez, Su Zhaoen, Austin James, Peter Selednik, Stuart Anderson, and Shunsuke Saito. Sapiens: Foundation for human vision models. In Euro- pean Conference on Computer Vision. pp. 206-228. Springer. 2024.
Inès Hyeonsu Kim, Seokju Cho, Jahyeok Koo, Junghyun Park, Jiahui Huang, Joon-Young Lee, and Seungryong Kim. Learning to track any points from human motion. arXiv preprint arXiv:2507.06233, 2025.
Tobias Kirschstein, Shenhan Qian, Simon Giehenhain, Tim Walter, and Mathhis NieBner. Nersem- ble: Multi-view radiance field reconstruction of human heads. ACM Transactions on Graphics (TOG), 42(4):114 2023.
Muhammed Kocabas, Nikos Athanasiou, and Michael J Black. Vibe: Video inference for human body pose and shape estimation. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 5253-5263, 2020.
Akshay Krishnan, Abhijit Kundu, Kevis-Kokitsi Maninis, James Hays, and Matthew Brown. Omninocs: A unified noes dataset and model for 3d lifting of 2d objects. In European Conference on Computer Vision, pp. 127-145. Springer, 2024.
Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. In European Conference on Computer Vision, pp. 71-91. Springer, 2024.
Bruno Lévy. Laplace-beltrani eigenfunctions towards an algorithm that" understands" geometry. In IEEE International Conference on Shape Modeling and Applications 2006 (SMI '06), pp. 13-13. IEEE, 2006.
Hongyang Li, Hao Zhang, Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, and Lei Zhang. Taptr: Tracking any point with transformers as detection. In European Conference on Computer Vision, pp. 57-75. Springer, 2024.
Hui Li, Zidong Guo, Seon-Min Rhee, Seungju Han, and Jae-Joon Han. Towards accurate facial landmark detection via cascaded transformers. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 4176-4185, 2022.
Tianye Li, Timo Bolkart, Michael. Black, Hao Li, and Javier Romero. Learning model of facial shape and expression from 4D seans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):194:1-1444 2017a. URL https:: http:://ooi.oog///././ /ttp//1...111/3110000000 3130813
Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning model of facial shape and expression from 4D scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia),
3666):19::1-194:17 2017b. URL http
14

---

Published as a conference paper at ICLR 2026
Minghua Liu, Mikaela Angelina Uy Donglai Xiang, Hao Su, Sanja Fidler, Nicholas Sharp, and Jun Gao. Partfield: Learning 3d feature fields for part segmentation and beyond. arXiv preprint arXiv:2504. 114504,.114,,, 2025.
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael Black. SMPL: A skinned multi-persen linear model. ACM Trans. Graphics (Proc. SIGGRAPH Asia), 34(6):248:12244 1-24:: 16, October 2015.
I Loshchilov. Decoupled weight decay regularization. arXiv preprint arXiv:1711.0510. 2017.
Camillo Lugaresi, Jiugiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, Juhyun Lee, al. Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:/906.00 08 72,, 2019a.
Camillo Lugaresi, Jiugiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, Juhyun Lee, et al. Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv: (000.09099988 011600817,,,. 2019b.
Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan. Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis. In 2024 International Conference on 3D Vision (3DV), pp. 800-809 IEEE, 2024.
Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, and Trevor Darrell. Diffusion Searching through time and space for semantic correspondence. In Advances in Neural Information Processing Systems, 2023.
hyperfeatures:
Zhixiang Min, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Enrique Dunn, and Manmohan Chan-
draker. Neuroes: Neural noes supervision for monocular 3d object localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21404-21444 21414,,,,, 2023.
Gycongsik Moon, Shoou-I Yu, He Wen, Takaaki Shiratori, and Kyoung Mu Lee. Interhand2. 6m: A dataset and baseline for 3d interacting hand pose estimation from a single rgb image. In European Conference Computer Vision, pp. 548-564, Springer, 2020.
Natalia Neverova, David Novotny, Mare Szafraniec, Vasil Khalidov, Patrick Labatut, and Andrea Vedaldi. Continuous surface embeddings. Advances in Neural Information Processing Systems, 33:17258-17270, 2020a.
Natalia Neverova, David Novotny, Mare Szafraniec, Vasil Khalidov, Patrick Labatut, and Andrea Vedaldi. Continuous surface embeddings. Advances in Neural Information Processing Systems, 33:17258-17270, 2020b.
Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang. Hu Xu, Vasu Sharma, Shang- Wen Li. Wojeiech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabricl Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Ar- mand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision, 2023.
Maks Ovsjanikov, Mirela Ben Ben-Chen, Justin Solomon, Adrian Butscher, and Leonidas Guibas. Func- tional maps: flexible representation of maps between shapes. ACM Transactions on Graphics (ToG), (1(4):1-11, 2012.
Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 5865-5874, 2021.
Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In 2009 sixth IEEE international conference on advanced video and signal based surveillance, pp- 296-301. Ieee, 2009.
Shenhan Qian. Vhap: Versatile head alignment with adaptive appearance priors, sep 2024. URL https://gi hww.uub..coom//eeeaaaaaaaaa an/VHAP
15

---

Published as a conference paper at ICLR 2026
Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, and Matthias Nießner. Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20299- 20309, 2024.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp- 8748-8763. PMLR, 2021.
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 1217912218, 2021.
Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, et al. Grounded sam: Assembling open-world models for diverse visual tasks. arXiv preprint arXiv:2401.14155 2024.
Emanuele Rodolà, Luca Cosmo, Michael M Bronstein, Andrea Torsello, and Daniel Cremers. Partial functional correspondence. In Computer graphics forum, volume 36, pp. 222-236. Wiley Online Library, 2017.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models, 2021.
Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6), November 2017.
Nataniel Ruiz, Eunji Chong, and James M Rehg. Fine-grained head pose estimation without key- points. In Proceedings of the IEEE conference on computer vision and pattern recognition work- shops, pp. 2074-2083, 2018.
Christos Sagonas, Georgios Tzimiropoulos, Stefanos Zafeiriou, and Maja Pantic. 300 faces in-the- wild challenge: The first facial landmark localization challenge. In Proceedings of the IEEE international conference on computer vision workshops, pp. 397-403, 2013.
Soyong Shin, Juyong Kim, Eni Halilaj, and Michael J Black. Wham: Reconstructing world- grounded humans with accurate 3d motion. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2070-2080, 2024.
Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang. Timothée Darcet, Théo Moutakanni. Leonel Sentana, Claire Roberts, Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie. Julien Mairal, Hervé Jégou, Patrick Labatut, and Piotr Bojanowski. DINOv3, 2025. URL htttps://aaxvv..ttt
abb/2508. 10104
Tomas Simon, Hanbyul Joo, lain Matthews, and Yaser Sheikh. Hand keypoint detection in single images using multiview bootstrapping. In CVPR, 2017.
Brandon Smart, Chuanxia Zheng, Iro Laina, and Victor Adrian Prisacariu. Splatt3r: Zero-shot gaussian splatting from uncalibrated image pairs. arXiv preprint arXiv:2408. 13912, 2024.
Edgar Sucar, Zihang Lai, Eldar Insafutdinov, and Andrea Vedaldi. Dynamic point maps: A versatile representation for dynamic 3d reconstruction. arXiv preprint arXiv:2503. /63188 2025.
Felix Taubner, Prashant Raina, Mathieu Tuli, Eu Wern Teh, Chul Lee, and Jinmiao Huang. 3d face tracking from 2d video through iterative dense uv to image flow. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 1227-1237, 2024.
Justus Thies, Michael Zollhofer, Mare Stamminger, Christian Theobalt, and Matthias Nießner. Face2face: Real-time face capture and reenactment of rgb videos. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2387-2395, 2016.
16

---

Published as a conference paper at ICLR 2026
Narek Tumanyan, Assaf Singer, Shai Bagon, and Tali Dekel. Dino-tracker: Taming dino for self- supervised point tracking in a single video. In European Conference on Computer Vision, pp- 367-385. Springer, 2024.
He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, and Leonidas J Guibas. Normalized object coordinate space for category-level 6d object pose and size estimation. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 2642-
2651, 2019.
Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. $294-5306 2025.
Shuzhe Wang. Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Ge- ometric 3d vision made casy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20697-20709, 2024.
Ting-Chun Wang, Arun Mallya, and Ming-Yis Yu Liu. One-shot free-view neural talking-head synthesis for video conferencing. In CVPR, 2021.
Philippe Weinzaepfel, Vincent Leroy, Thomas Lucas, Romain Brégier, Yohann Cabon, Vaibhav Arora, Leonid Antsfeld, Boris Chidlovskii, Gabriela Csurka, and Jérôme Revaud. Croco: Self- supervised pre- training for 3d vision tasks by cross-view completion. Advances in Neural Infor- mation Processing Systems, 35:3502-3516, 2022.
Yaniv Wolf, Amit Bracha, and Ron Kimmel. Gs2mesh: Surface reconstruction from gaussian splat- ting via novel stereo views. In European Conference Computer Vision, pp. 207-224. Springer, 2024.
Yiqian Wu, Jing Zhang. Hongbo Fu, and Xiaogang Jin. Lpff: A portrait dataset for face generators across large poses. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 20327-20337, 2023.
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Seg- Former: Simple and efficient design for semantic segmentation with transformers. Advances in neural information processing systems, 34:12077-12090 2021.
Chao Xu, Ang Li, Linghao Chen, Yulin Liu, Ruoxi Shi, Hao Su, and Minghua Liu. Sparp: Fast 3d object reconstruction and pose estimation from sparse views. In European Conference on Computer Vision, pp. 143-163. Springer. 2024.
Yang You, Yujing Lou, Chengkun Li, Zhoujun Cheng, Liangwei Li, Lizhuang Ma, Cewu Lu, and Weiming Wang. Keypointnet: A large-scale 3d keypoint dataset aggregated from numer- ous human annotations. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recog- nition (CVPR), pp. 13644-13653, 2020. URL https::/aptt wwwanticschooeeeeee..eeooooccc org/
Yuanwen Yue, Anurag Das, Francis Engelmann, Siyu Tang, and Jan Eric Lenssen. Improving 2D Feature Representations by 3D-Aware Fine- Tinn-nn In European Conference on Computer Vision (ECCV), 2024.
Hao Zhang, Mengmeng Wang, Yong Liu, and Yi Yuan. Fdn: Feature decoupling network for head pose estimation. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pp- 22789-12796, 2020.
Junyi Zhang. Charles Herrmann, Junhwa Hur, Luisa Polania Cabrera, Varun Jampani, Deqing Sun, and Ming-Hsua Yang. tale two features: Stable diffusion complements dino for zero-shot semantic correspondence. Advances in Neural Information Processing Systems, 36:45533 45547, 2023a.
Junyi Zhang, Charles Herrmann, Junhwa Hur, Eric Chen, Varun Jampani, Deqing Sun, and Ming- Hsuan Yang. Telling left from right: Identifying geometry- semantic correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- 3076-3085, 2024a.
17

---

Published as a conference paper at ICLR 2026
Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, De- qing Sun, and Ming-Haae Hsuan Yang. Monst3r: A simple approach for estimating geometry in the 2024b.
presence of motion. arXiv preprint
Longwen Zhang, Zijun Zhao, Xinzhou Cong, Qixuan Zhang, Shuqi Gu, Yuchong Gao, Rui Zheng, Wei Yang, Lan Xu, and Jingyi Yu. Hack: Learning a parametric head and neck model for high- fidelity animation. ACM Transactions on Graphics (TOG), 42(4):1-200 2023b.
Xiaozheng Zheng. Zhuo Su, Chao Wen, Zhou Xue, and Xiaojie Jin. Realistic full-body tracking from sparse observations via joint-level modeling. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14678-14688, 2023a.
Yang Zheng, Adam W Harley, Bokui Shen, Gordon Wetzstein, and Leonidas Guibas. Pointodyssey: large-scale synthetic dataset for long-term point tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 19855-19865, 2023b.
Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in visual- linguistic manner. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 18697-18709, 2022a.
Yinglin Zheng, Hao Yang. Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in visual- linguistic manner. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18697-18709, 2022b.
Hao Zhu, Wayne Wu, Wentao Zhu, Liming Jiang, Siwei Tang, Li Zhang, Ziwei Liu, and Chen Change Loy. Celebv-hq: A large-scale video facial attributes dataset. In European con- ference on computer vision, pp. 650 350-667.7 Springer, 2022.
Junzhe Zhu, Yuanchen Ju, Junyi Zhang, Muhan Wang. Zhecheng Yuan, Kaizhe Hu, and Huazhe Xu. Densematcher: Learning 3d semantic correspondence for category-level manipulation from a single demo. arXiv preprint arXiv:2412.05266 2024.
Xiangyu Zhu, Zhen Lei, Xiaoming Liu, Hailin Shi, and Stan Li. Face alignment across large poses: 3d solution. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 146-155, 2016.
Xiangyu Zhu, Xiaoming Liu, Zhen Lei, and Stan 7.i. Face alignment in full pose range: A 3d total solution. IEEE transactions on pattern analysis and machine intelligence, 41(1):78-9) 92, 2017.
Wojeiech Zielonka, Timo Bolkart, and Justus Thies. Towards metrical reconstruction of human faces. In European conference computer vision, pp. 250-269. Springer, 2022.
18

---

Published as a conference paper at ICLR 2026
A APPENDIX
Use of LLMs. We used LLMs for expanding our knowledge regarding the latest related work.
B ABLATION STUDIES
To avoid computational burden, 50% of the available training steps, which, in our experience. is usually sufficient for decent convergence.
B.1 Loss WEIGHTS
Table 4: Ablation study on loss weights Atmks and Aseg-
Method
MAE
RMSE
ArcFace
Met3R +
Ours (Ainks
50, Aang
3.85
6.17
0.388
0.384
Atmks 10
5.35
8.68
0.390
0.413
Aimks 250
3.92
6.29
0.393
0.387
Azeg 0
3.68
5,86
0.353
0,420
Aeeg = 0.2
3.72
5,95
0.400
0.374
Aseg 5
3.98
6.39
0.384
0.400
We provide quantitative ablation study on loss weights Atmka and Aseg in Table 4 We observe that increasing Aimks does further improve the metries, and making it too low (Atmks 10) results in inferior model performance. For the segmentation loss weight, lowering Aseg to 0.2 yields slightly better performance on both single-person and cross-person benchmarks. As shown in Fig. our model exhibits close canonical space structure to the Aseg = 0.2 baseline. However, removing the segmentation loss entirely (Aseg = 0) leads to degraded cross-person metrics, indicating the importance of semantic supervision.
B.2 SMOOTHING STRATEGIES
We compare different smoothing strategies applied to the latent feature grid E in Table 5 We evaluate four approaches: (1) no smoothing: (2) uniform smoothing with the same kernel size (7x7); (3) 3D bilateral filter with da 1.5 (spatial difference) and a, 0.1 (intensity difference), selected from a grid search over [0.05, 0.1, 0.2, 0.4); (4) Gaussian smoothing (our method) with 1.5.
Overall, the quantitative evaluation confirms the importance of using a smooth canonical space, and the specific filter to appears not to be particularly important.
Without smoothing, the model is still able to roughly locate correct regions in the canonical space, but the space is harder use directly for querying or region finding, evidenced by the less con- sistent region boundaries (see Fig.
B.3 GRID RESOLUTION AND SMOOTHING STRENGTH
We analyze the effect of grid resolution No (the number of voxels in the voxel grid by each side) and Gaussian smoothing strength a in Table 6 We observe a slight improvement in geometric metrics (MAE, RMSE) with lower sigma value (a = 0.5). However, the best variant according to cross- person metries (AreFace, Met3R) achieved with with N₂ 128 for Mct3R (as in our final method). Overall, the metrics do not seem to deviate significantly with variations in these parameters.
1.5, either with N₂
for ArcFace or
Memory consumption. Memory occupied by the voxel grid grows cubically with Na. The method takes approximately 16 GB GPU memory for N₂ = 128 and 14 GB for No = 64. Increasing Na further significantly increases memory requirements approximately 32 GB. Sigma does not affect memory consumption. We were unable to rum experiments with N₂ = 256 due to constraints on available GPU memory.
19

---

Published as a conference paper at ICLR 2026
Ours
Ours 0.2x Aseg
Ours
Ours 0.2x Aseg
Figure 9: Semantic regions on head images can be located via selecting corresponding volumetric regions in the canonical space. Blue: forehead center, green and orange: ears, yellow: skin near the left eyebrow corner. Reducing segmentation loss weight Xaeg results in a similar structure of the canonical space.
Table 5: Ablation study on different smoothing strategies.
Method
MAE
RMSE
ArcFace
Met3R |
No smoothing
4.22
6.71
0.387
0.393
Uniform smoothing
3.83
6.14
0.383
0.381
Bilateral filter
3.97
6.35
0.392
0.390
Gaussian smoothing (Ours)
3.85
6.17
0.388
0.384
B.4 POINT-TRACKER ABLATION
By default, CoTracker filters points by discarding them if their estimated confidence below threshold of 0.6; this threshold was also used in our experiments. Here, we demonstrate what hap- pens when stricter filtering with larger threshold applied, thereby removing more outliers. Re- sults are shown in Z
In line with our observations on the stability of the point tracker, increasing the threshold mildly improves the results, as it removes outliers. With stronger threshold of 0.75 and 0.9, obtain performance similar to that of 0.6, which is used in our model. With a very strict threshold of 0.98, the model's 's performance slightly improves indeed. However, if we increase the threshold further (0.99), too many non-outliers are excessively filtered out, and no further improvement is observed.
Finally, observe slight performance boost when more recent trint-tracker is used for data acquisition (see Table 8). For fair comparison, we use the same initializations for point tracks as in Cotracker3.
20

---

Published as a conference paper at ICLR 2026
Ours
No smoothing
Ours
No smoothing
Figure 10: Semantic regions head images can be located via selecting corresponding volumetric regions in the canonical space. Blue: forehead center, green and orange: ears, yellow: skin near the left eyebrow corner. Adding smoothing makes region finding more reliable.
Table 6: Ablation study on grid resolution Na and Gaussian smoothing strength a.
Method
MAE 4
RMSE J
ArcFace f
Met3R +
NJ 64, a
3.65
5.86
0.391
0.390
NJ 64, a = 1.5
4.06
6.51
0.392
0.385
N₂ 64, σ 4.5
4.04
6.49
0.391
0.385
Na 128 (ours), 0.5
3.64
5,80
0.390
0.386
Na 128 (ours), σ 1.5 (ours)
3.85
6.17
0.388
0.384
NJ 128 (ours), 4.5
3.89
6.25
0.386
0.386
C ROBUSTNESS TO LANDMARKS AND SEGMENTATION MASKS
We further evaluate robustness to errors in landmarks and segmentation masks by adding Gaussian noise to landmarks and dilating randomly selected segmentation classes using a x kernel. Dense- Marks remains robust to large mask dilations (48 px) and strong landmark noise (up to = 16 px), showing greater tolerance these errors than to track inaccuracies. Results shown in Table 9
D IMPORTANCE OF THE CANONICAL SPACE, DENSE WARPING
We provide additional results ablating the use of the canonical space in our method dense warping in Fig. [I Using DenseMarks space provides better geometric awareness, as evidenced by the better consistency of the blended images.
E EXPERIMENTS ON CHAIRS
To demonstrate the applicability of our method to other domains beyond human heads, we retrained approach sample category of generic objects, specifically, chairs from the ShapeNet (Chang
21

---

Published as a conference paper at ICLR 2026
Table 7: Effect different confidence thresholds for filtering CoTracker points.
Method
MAE 4
RMSE
ArcFace
Met3R
Confidence threshold 0.6 (ours)
3,85
6.17
0.388
0.384
Confidence threshold 0.75
3.76
6.00
0.384
0.386
Confidence threshold 0.9
3,81
6.11
0.385
0.389
Confidence threshold =0.98
3.63
5.81
0.390
0.388
Confidence threshold 0.99
3.76
6.01
0.389
0.399
Table 8: Stability across different point trackers. We show results with the best confidence threshold (conf.thr.) for each point-tracker.
Method
MAE +
RMSE +
ArcFace t
Met3R +
Cotracker3 (Karaev et al. 2024a) (conf.thr. thr. = 0.98)
3.63
5.81
0.390
0.388
Alltracker (Harlyy et 2025 (conf.thr. 0.8)
3.60
5.75
0.392
0.382
al. 2015) dataset. For dataset preprocessing, we sampled 200 random points on each object and obtained multi-view correspondences by rendering these points onto a fixed set of 20 orbital viewpoints. We additionally use object masks train our model. Overall, we obtain approximately 1000 object samples for training.
We apply the same architecture and training procedure as described in the main experiments, with the following modifications for the chairs domain. For regularization of the canonical space, use keypoints obtained by KeypointNet Youu et al., 2020) instead of Mediapipe landmarks used for heads. For augmentation, reduce overfitting. The model is trained for 25K steps, fewer than in the head experiments due to the smaller dataset and the synthetic nature of the data.
random rotation augmentation
[0, 90, 180, 270] degrees to
We demonstrate the learned canonical coordinates (see Fig 12 and dense warping (see Fig. 13 While the results can be further improved with more extensive experiments, these initial findings suggest that our approach holds promise when applied to other domains.
F HEAD POSE ESTIMATION
We demonstrate head pose estimation as an additional application of our algorithm. We design head pose regression model that accepts monocular image represented by its DenseMarks em- beddings as input and produces its 3-DoF head pose as output. By following an established head pose estimation benchmark (Hempel et al. 2022 Ruiz et al. 2018 Zhang et al. (020), train our head pose estimation model on the 300W-LP dataset (Zhu et al. 2016) and evaluate it on the AFLW2000-33 33 dataset (Zhu al. 2016).
In the table below, we demonstrate that, compared to raw RGB imput, using DenseMarks embeddings input results in 3x lower angular error for standard lightweight regressor MobileNet (Howard jet al. 2017). We further observe that the head pose can be estimated from DenseMarks input by reducing the regressor to just single fully-connected layer, while this does seem to be possible from RGB input.
For all experiments, training was performed using MAE loss, averaged three angles, and learn- ing rate of le-4. To adapt to more varying cropping in AFLW2000-3D than in 300W-LP, random crop and zoom were added as augmentations in all experiments. Positional encoding (sine/cosine- based) is added as an extra imput channel in all experiments. 90% of 300W-LP were used for training and 10% for validation. As quality reference, one of the most recent methods (Hempel al. 2022) achieves Avg MAE of 3.47 on AFLW2000-3D after training on the full 300W-LP.
G SYNTHETIC STRESS TESTS
To evaluate the robustness of our method various image perturbations, we conduct synthetic stress tests the IPFF (Wu et al. 2023) dataset. We apply four types of augmentations test the stability
22

---

Published as a conference paper at ICLR 2026
Table 9: Stability of Densemarks the errors of the off-the-shelf sparse landmarks predictor (left) and segmentation masks (right).
Method
MAE
RMSE
AreFace
Met3R
MAF.]
RMSE
ArcPace
16
4,33
6.92
0.387
0.386
Dilation I
3,99
6.37
0.381
0.382
4.15
6.63
0.384
0.392
Dilation x8
3.84
6.15
0.389
0.383
3.84
6.14
0,380
0,389
Dilation x4
3.80
0.388
0.382
Ours (or
3.85
6.17
0.388
0.384
Ours (no dilation)
3.85
6.17
0.388
0.384
Source
DINOv3
Source
DINOv3
Figure 11:
Dense warping, even rows: comparison of our method with DINOv3 fine-tuned. Odd rows: mapping of meshgrid-like coordinates, blended with RGB. Canonical space boosts geometric consistency, indicated by smoother warping.
of correspondences under challenging conditions: motion blur, illumination changes, color shifts, and synthetic occlusions.
We used 300 images from the LPFF dataset, containing diverse poses and appearances. We applied
four types augmentations: (I) motion blur with an intensity range from 15 px to 25 px; (2) illumination changes with brightness and contrast adjustments in the range of -40% to 40%; (3) color shifts with RGB shift in the range of -30 to 30; (4) synthetic occlusions covering the image with uniformly colored rectangles average by 10%.
For each augmentation type, we apply the augmentation 8 times to each source image and find the nearest neighbors in the original source image. We then fit Gaussian distribution the resulting nearest neighbor locations compared with the center in the corresponding pixel. We report three metrics: (I) the mean absolute error (MAE) in pixels; (2) the empirical standard deviation asd of the set of estimates in pixels; (3) the entropy calculated using the obtained standard deviation by the formula for the entropy of fitted isotropic Gaussian distribution:
In(2red? Among all input image manipulations, our method seems to be the most sensitive to synthetic occlusion (see Table TT). We find that natural, given that such occlusions were not present in the training data. Nev- ertheless, even under synthetic occlusion, the standard deviation of the estimated locations remains relatively small (less than pixels). We assume that the entropy can be reduced by incorporating these augmentations into the training process. We provide visualizations of dense warping under lightning change in Fig. 14
H ADDITIONAL FAILURE CASES FOR WARPING
We have manually selected challenging images from the LPFF dataset (Wu et al. 2023) with volu- minous hairstyles, hats, and masks, and obtained dense warping using correspondences estimated by method (see Fig. 15). We have not identified significant failure cases related to skin tone differ- ences. Accessories and long hair may constitute failure case, as the correspondences on them may not always be geometrically consistent, resulting in unnatural distortions the resulting warps. We
23

---

Published as a conference paper at ICLR 2026
T
Figure 12: Canonical coordinates for chairs. We learn the canonical space on chairs and visualize
RGB and predicted coordinates.
Source
Target
Ours
Source
Target
Ours
Dense warping. Here, we copy pixels from source to target based on the target ssource nearest neighbors search in the space of embeddings, predicted by our model.
Figure 13:
believe that method can potentially benefit from the inclusion of even more diverse datasets in training to alleviate problems in challenging regions.
I COMPARISON WITH DENSE FACE METHODS
We did not consider face 3DMM-relater baselines (Feng et al., 2018) since they do not provide semantics outside of the face region and cannot be directly used for hair and accessories. For com-
24

---

Published as a conference paper at ICLR 2026
Table 10: Head pose estimation results on 300W-LP validation set and AFLW2000-3D test set.
Yaw MAE
Pitch MAE
Roll MAE
Avg MAE
Yaw MAE
Piich MAE
Rell MAE
Avg MAE
RGB -+ PC layer
$0.59*
9.3
11.38
23.93
30.92
18.77"
17.97
22.53"
DenseMarks FC layer
4,30"
5.45"
6.72
5.49
15.00"
13.26'
12.58
13.61"
RGB MobileNee
4.06
7,09"
27.74"
10.57
16,37
18.22
MobileNet
2.75
Table 11: Synthetic stress test results on LPFF dataset.
Method
MAE
Std setd 4
Entropy
Ours (motion blur)
2.87
3.57
5.38
Ours (illumination change)
1.44
2.33
4.53
Ours (color shift)
0.53
0.77
2.30
Ours (occlusions)
3.44
5.89
6.38
Ours (all)
2.1
4.26
5.72
Souree
Targot
Ours
Source
Target
Ours
Figure 14: Predicted canonical coordinates robust to lighting changes. Ours: result of target->source dense warping.
pleteness of the evaluation, we compare with the PRNet baseline (Feng et al. 2018), which predicts coordinates of the Basel Face Model (Paysan et al., 2009). Since it only covers the face region, develop a heuristic that allows us to compare face 3DMM-related methods outside the face region well, for the sake of fair comparison. Namely, for every pixel (i, in the face region, lever- age [u. v. 0] as embedding, and outside of it, we use [Unear, Unear, dist), where (Unear: Unear) are UV coordinates of the nearest pixel covered by PRNet, and dist stands for the normalised distance between the pixel (i,j) and the nearest covered pixel. This way, we cover the whole head region with embeddings.
We additionally present values of the PCK measure to quantify the accuracy of correspondences computed across distinct subjects, since ArcFace and Met3R lack true correspondence accuracy, For this, we manually annotated landmarks for 30 random subjects in the face regions (ears, eyebrows, hairline- 15 unique points) on the LPFF dataset (Wu al. 2023). which includes a wide range of extreme poses/angles and diverse appearances. This gives 400 unique pairs for evaluation.
As we show in Table 12 PRNet performs worse over the same person's correspondence benchmarks compared to our strongest baseline (DINOv3 fine-tuned) and Ours. We observe strong performance of PRNet over cross-person correspondences. Both ArcFace and Met3r seem to strongly rely on the face region, as it constrains the most distinctive features. Additionally, Met3r measures view- consistency, which can be higher the object is simpler. Nevertheless, our model demonstrates superior performance with respect the PCK-based benchmarks, which implies more accurate face/head understanding. A potential improvement direction for DenseMarks would be to addition- ally leverage data with only the face region observed, similar to the data used for training PRNet.
25

---

Published as a conference paper at ICLR 2026
Source
Targel
Ours
Source
Tarpet
Ours (3)
Figure 15: Dense warping. Here, we copy pixels from source to target hased on the target >souree nearest neighbors search the space of embeddings, predicted by each model. Our method may struggle to warp long hair, hats, and face masks, resulting in unnatural distortions.
Table 12: Comparison with face 3DMM-related baseline.
Same-perse
Cross-person
MAE +
RMSE J.
PCK 1KK@r00.05 f
ArcFace
Mct3R J.
PCK
PRNet
7.33
11.70
0.72
0.462
0.365
0.76
DINOv3 fine-tuned
6.35
10.20
0.85
0.348
0.455
0.69
Ours
3.68
5.90
0.90
0.384
0.388
0.85
J VISUALIZATION OF POINT TRACKS
We show visual examples of the generated point tracks in Fig. 16 dataset, CelebV-HQ. point tracks are mostly accurate. We performed a manual examination for subset of 100 videos and found that approximately 9% of the tracks are incorrectly predicted as invisible and around 3 of the tracks have visually noticeable drift. Most of the missing tracks originate from comparatively less textured surfaces such hair, clothes, or headwear. On our training dataset, Celeb- CHQ, tracks are predominantly accurate, even on these challenging surfaces. This reliability is in part due to the high resolution of the head region in the videos and the typically smooth head motion (e.g.. interview-style and film shooting data), which allows the point tracker to produce reliable correspondences.
We found that on our training
26

---

Published as a conference paper at ICLR 2026
Figure 16: Point-track visualizations on CelebV-HQ dataset; each row shows two videos with three
frames each. Starting from i tracks are tracked through f3. Missing tracks are indicated by the red color.
27


Reviews:
[{"review_id": "wmW1fwXoR8", "reviewer": "Reviewer_F5bW", "summary": "This paper introduces DenseMarks, a learned dense representation for human head correspondence and tracking. Unlike traditional landmark- or region-based approaches that are limited to sparse and often skin-only regions, DenseMarks learns a per-pixel embedding space that maps every head pixel into a shared 3D canonical unit cube. The canonical embedding provides an interpretable, queryable, and spatially consistent representation across poses, expressions, and individuals, enabling applications such as dense semantic correspondence, 3D morphable head tracking, and view-consistent reconstruction.\n\nThe method leverages a Vision Transformer (ViT) backbone that predicts per-pixel embeddings guided by multiple complementary objectives:\n1.a contrastive loss based on point correspondences estimated from talking-head videos,\n2.semantic supervision from face landmarks and segmentation maps, and\n3.a spatial smoothness constraint via a 3D Gaussian-regularized latent feature cube.\n\nTogether, these components yield a structured latent space that is both interpretable and geometrically consistent, capturing the full head region, including challenging elements such as hair and accessories. Experimental results show promising performance in dense geometry-aware matching and monocular head tracking, outperforming existing pretrained vision foundation model (VFM) baselines.", "questions": "Is it possible to extend the construction of this representation to the whole body or even to objects in general?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Conceptually strong and well-motivated reformulation of dense correspondence into a 3D canonical latent space. Also, the multi-task supervision yields a highly structured and interpretable representation. Technically sound training strategy that leverages point-tracking data without requiring explicit 3D ground truth.\n2. Demonstrates robust performance across pose and occlusion variations, covering the full head including non-skin regions. This paper is easy to understand, and the video instructions are also very clear.", "weaknesses": "Method.   \nThe approach depends on the quality and stability of off-the-shelf 2D point trackers, which may introduce bias or drift in contrastive supervision. \n\nExperiment.  \nExperimental fairness may be affected since the proposed model is trained specifically on face-centric data, while the compared baselines are typically pretrained on broader, more general datasets. This domain bias could partially account for the observed performance gap.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RCWh2S48E4", "reviewer": "Reviewer_DAuy", "summary": "This paper proposes DenseMarks, a new learned representation for the human head. The method uses a Vision Transformer to predict a 3D embedding for each pixel of an input image. In addition, the authors collect a dataset of pairwise point matches and employ multi-task learning with facial landmarks and segmentation constraints. The proposed representation can be applied to multiple downstream tasks. Experiments demonstrate state-of-the-art performance in geometry-aware point matching and monocular head tracking with 3D Morphable Models.", "questions": "**Questions:**\n\nN/A\n\n**Additional comments:**\n\nIt would be beneficial for the author to further explore the application of DenseMarks on additional head-related tasks such as head pose estimation [1], which requires both geometric and semantic understanding of head structures. This would provide stronger evidence supporting the generality and effectiveness of the DenseMarks representation.\n\n[1] Algabri, Redhwan, Ahmed Abdu, and Sungon Lee. \"Deep learning and machine learning techniques for head pose estimation: a survey.\" Artificial Intelligence Review 57.10 (2024): 288.", "limitations": "", "rating": 10, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- **High novelty and insightful contribution:**\nThe paper introduces DenseMarks, a novel dense representation for human heads that:\n(1) enables high-quality dense correspondences across complete head regions, including irregular features such as hair and accessories;\n(2) achieves robust tracking under challenging conditions such as severe occlusions; and\n(3) produces a structured, interpretable, and smooth canonical latent space that supports further exploration and interaction.\n\nCompared with conventional sparse landmark representations, DenseMarks demonstrates remarkable novelty and conceptual depth. Its design is ingenious and inspiring, introducing a unified dense representation that not only achieves fine-grained head correspondence but also exhibits strong generalization and versatility across diverse downstream tasks such as tracking, reconstruction, and semantic understanding. This elegant formulation presents a foundational and broadly applicable idea, with clear potential for extension beyond head modeling to full-body representations and other related domains.\n- **Comprehensive related work:**\nThe authors provide a thorough and well-organized review of related research, demonstrating a deep understanding of the field and clearly situating their contribution within existing literature.\n- **Excellent presentation:** The paper includes clear and informative visualization figures and is well written, clearly structured, and compact.\n- **Extensive experiments:** The experimental design is comprehensive and thorough, providing strong evidence for the effectiveness and generality of DenseMarks.\n- **Excellent visualizations demonstrating the effectiveness of DenseMarks:** Visualization results such as point querying, semantic region mapping on head images, and dense warping clearly show that DenseMarks captures rich and interpretable head semantics and generalizes well across various head-related tasks, including monocular tracking and stereo reconstruction.", "weaknesses": "N/A", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "83BIyLZDw1", "reviewer": "Reviewer_99jL", "summary": "The paper proposes DenseMarks, a method to learn dense canonical 3D embeddings for human head images. A ViT network predicts per-pixel 3D locations in a canonical unit cube, trained using point tracks from CoTracker3 on talking head videos with contrastive loss plus landmark and segmentation constraints. The method shows improvements over foundation models in head tracking and correspondence tasks.", "questions": "- How does tracking quality from CoTracker impact the learned representation? Can you provide quantitative analysis of pseudo-GT reliability and explore filtering strategies?\n- Have you considered hard negative mining or sampling strategies that encourage learning from more diverse poses/expressions within the contrastive framework?\n- How does the method generalize to truly in-the-wild videos with extreme poses, fast motion, or severe occlusions beyond the interview-style training data?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Sound overall pipeline** The approach of using point tracks for supervision combined with a 3D canonical space is reasonable and well-motivated\n- **Comprehensive evaluation** Good experimental validation across multiple tasks (monocular tracking, dense warping, point querying, application to downstream models, etc. with strong baselines\n- **Interpretable representation** The 3D cube canonical space with semantic structure is simple and queryable\n- **Practical improvements** Demonstrates clear improvements on downstream tasks like monocular head tracking with 3DMMs", "weaknesses": "- **Pseudo-GT Quality** CoTracker3 is not optimized for heads, yet no analysis of pseudo-GT quality or its impact on training. Critical questions remain: How do tracking errors affect learning? Should low-quality tracks be filtered? How reliable are tracks on challenging regions (hair, accessories)?\n- **Limited training data diversity** CelebV-HQ contains interview-style videos with predominantly frontal/near-frontal poses and limited expression variations. Impact on generalization to extreme poses unclear. Only 100 held-out videos from the same distribution used for evaluation\n- **Weak contrastive learning setup** Sampling pairs from the interview video means pose/expression/shape are very similar, making the task easier but potentially less robust. The current setup may not learn discriminative features for challenging cases\n- **Limited novelty** The method is more an engineering / pipeline work: standard ViT training with contrastive loss and auxiliary constraints. The main contribution is applying existing components (CoTracker + contrastive learning) to heads with a 3D cube representation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "I49RFNXgdD", "reviewer": "Reviewer_zpxw", "summary": "The article proposes DenseMarks, a per-pixel embedding of head images into a 3D canonical unit cube. A ViT-based embedder maps each pixel to a 3D coordinate. These coordinates learned a volumetric latent grid that is smoothed with a 3D Gaussian to impose spatial continuity and interpretability. Training uses siamese supervision from automatically extracted point correspondences (CoTracker3) over in-the-wild talking-head videos, plus auxiliary landmark and segmentation losses to anchor semantics (300W landmarks via Mediapipe; parsing via FaRL/SegFormer). The embedding is intended to yield dense, semantics-aware correspondences robust to pose/occlusion, including hair and accessories. Experiments include qualitative point querying, dense warping comparisons to DINOv3, Sapiens, Diffusion Hyperfeatures, Fit3D, quantitative evaluation with ArcFace/Met3R on Nersemble, an application to monocular head tracking, and a small stereo reconstruction demo.", "questions": "Missing results on 2D EPE (in pixels) and PCK@$\\tau$ on projected mesh vertices for same-person pairs (Nersemble), and part-wise accuracy (ears, hairline, eyebrows) using manual landmarks? This would complement ArcFace/Met3R with true correspondence accuracy.\n\nMissing comparison to dense face UV / canonical correspondence [1, 2] (e.g., PRNet-style [3] face UV maps), CSE/functional-map-style human correspondences, and any dense face parsing + nearest-neighbour baselines. Also, add VFM+learned projection heads trained with the same track supervision to separate the value of canonicalization from pure feature learning.\n\n[1] Alp Guler, R., Trigeorgis, G., Antonakos, E., Snape, P., Zafeiriou, S. and Kokkinos, I., 2017. Densereg: Fully convolutional dense shape regression in-the-wild. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6799-6808).\n\n[2] Guo, J., Zhu, X., Yang, Y., Yang, F., Lei, Z. and Li, S.Z., 2020, August. Towards fast, accurate and stable 3d dense face alignment. In European Conference on Computer Vision (pp. 152-168). Cham: Springer International Publishing.\n\n[3] Feng, Y., Wu, F., Shao, X., Wang, Y. and Zhou, X., 2018. Joint 3d face reconstruction and dense alignment with position map regression network. In Proceedings of the European conference on computer vision (ECCV) (pp. 534-551).\n\nProvide a grid of ($N^d$, $\\sigma$) vs accuracy/warp smoothness and memory, test no smoothing / different smoothers. This will validate that the canonical bottleneck (E + smoothing) is the right bias.\n\nQuantify how tracker noise and parsing/landmark errors affect training (e.g., by injecting synthetic correspondence noise, or filtering by CoTracker confidence). Show stability across different point-trackers.\n\nEvaluate cross-dataset (e.g., Nersemble images for testing only, or other head datasets) and stress tests (pose extremes, occlusions, motion blur, illumination changes). Report calibration/uncertainty of matches (entropy of nearest-neighbours’ distributions) under shift.\nProvide quantitative curves for $λ_{lmks}$, $λ_{segm}$, and study negative-pair sampling in the contrastive loss (within-frame vs cross-frame; hard-negative mining).\n\nShow representative failure cases (e.g., voluminous hairstyles, hats, masks, skin tones) and per-attribute breakdown to understand biases inherited from CelebV-HQ and the pseudo-label tools.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "A compact 3D canonicalization with an explicit volumetric latent grid and smoothing makes the embedding queryable and visually interpretable. The design aims to cover the entire head (incl. hair/accessories) rather than just landmarkable skin.\n\nAvoids expensive 3D ground truth by leveraging point tracks from off-the-shelf trackers (CoTracker3) on large talking-head corpora. It integrates landmark and parsing constraints to stabilize semantics.\n\nShows tangible improvement when augmenting a parametric 3DMM tracker (VHAP + FLAME) with a DenseMarks photometric term, especially at extreme poses/occlusions, indicating practical usefulness in monocular tracking.\n\nDespite using 3D coordinates as the embedding key, it seems to produce cleaner dense warps than high-dimensional VFM features (DINOv3, Sapiens, DHFeats, Fit3D) in qualitative demos. The ArcFace/Met3R table suggests better same-person geometric consistency.", "weaknesses": "The comparisons target generic dense features (DINOv3/Sapiens/DHFeats/Fit3D) rather than head-specific dense correspondence baselines (e.g., UV/texture-coordinate regressors for faces, functional-map-based human correspondences, dense face/ear/hair parsing and matching). Without task-matched baselines, SOTA claims are difficult to substantiate.\n\nTraining depends on CoTracker3 tracks, Mediapipe landmarks, and FaRL/SegFormer parsing, etc. These are all off-the-shelf outputs with their own biases/failure modes. The paper does not quantify noise tolerance or show that DenseMarks isn’t just using these tools’ error modes (e.g., failure on occlusions, hairstyles, accessories).\n\nArcFace similarity and Met3R measure identity or multi-view image consistency is not explicit correspondence accuracy. The paper lacks standard geometric metrics such as 2D endpoint error (on projected mesh vertices), PCK/NME on annotated parts, or reprojection errors with GT geometry. The Nersemble evaluation uses GS2Mesh meshes as a proxy, but the reported table aggregates identity/consistency rather than direct correspondence accuracy distributions.\n\nCurrent evidence is largely qualitative. Claims of robustness to occlusion/pose/appearance would benefit from controlled experiments (synthetic occlusions, blur, lighting/colour shifts) and cross-dataset generalization beyond CelebV-HQ/Nersemble. \n\nThe paper introduces a 3D latent grid E with Gaussian smoothing ($\\sigma$) but does not provide an ablation study for grid resolution ($N^d$), $\\sigma$, or memory/speed trade-offs.\n\nOnly a qualitative ablation removes landmark or segmentation losses. There’s no quantitative study of $λ_{lmks}$ / $λ_{segm}$ (Eqn. 1), or of how correspondence quality moves with/without each term, nor an analysis of the negative sampling strategy in the contrastive term.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)