Sapiens2
Authors: Rawal Khirodkar
He Wen
Julieta Martinez
Yuan Dong
Zhaoen Su
Shunsuke Saito
Venue: ICLR 2026 Poster (2026)
Keywords: computer vision
human pose
segmentation
transformers
foundation models
Primary area: foundation or frontier models, including LLMs
TL;DR: High resolution transformers for human-centric images.

Abstract: We present Sapiens2, a model family of high-resolution transformers for human-centric vision focused on generalization, versatility, and high-fidelity outputs. Our model sizes range from 0.4 to 5 billion parameters, with native 1K resolution and hierarchical variants that support 4K. Sapiens2 substantially improves over its predecessor in both pretraining and post-training. First, to learn features that capture low-level details (for dense prediction) and high-level semantics (for zero-shot or few-label settings), we combine masked image reconstruction with self-distilled contrastive objectives. Our evaluations show that this unified pretraining objective is better suited for a wider range of downstream tasks. Second, along the data axis, we pretrain on a curated dataset of 1 billion high-quality human images and improve the quality and quantity of task annotations. Third, architecturally, we incorporate advances from frontier models that enable longer training schedules with improved stability. Our 4K models adopt windowed attention to reason over longer spatial context and are pretrained with 2K output resolution. Sapiens2 sets a new state-of-the-art and improves over the first generation on pose (+4 mAP), body-part segmentation (+24.3 mIoU), normal estimation (45.6% lower angular error) and extends to new tasks such as pointmap and albedo estimation.

Published as a conference paper at ICLR 2026
SAPIENS2
Rawal Khirodkar, He We, Julieta Martinez, Yuan Dong, Su Zhaoen, Shunsuke Saito Meta Reality Labs https: http://ggtthbboooo htcp///ooooorrrarccc/aaa........
ABSTRACT
We present Sapiens2, model family of high-resolution transformers for human- centric vision focused on generalization, versatility, and high-fidelity outputs. Our model sizes range from 0.4 to 5 billion parameters, with native 1K resolution and hierarchical variants that support 4K. Sapiens2 substantially improves over its pre- decessor in both pretraining and post-training. First, to learn features that capture low-level details (for dense prediction) and high-level semantics (for zero-shot few-label settings), we combine masked image reconstruction with self-distilled contrastive objectives. Our evaluations show that this unified pretraining objec- tive is better suited for a wider range of downstream tasks. Second, along the data axis, we pretrain on curated dataset of 1 billion high-quality human images and improve the quality and quantity of task annotations. Third, architecturally, we incorporate advances from frontier models that enable longer training schedules with improved stability. Our 4K models adopt windowed attention to reason over longer spatial context and are pretrained with 2K output resolution. Sapiens2 sets a new state-of tf-the--rt and improves over the first generation on pose (-4 mAP), body-part segmentation ( (+24.33 mloU), normal estimation (45.6% lower angular error) and extends to new tasks such as pointmap and albedo estimation.
INTRODUCTION
Sapiens introduced foundation model for human-centric vision (Khirodkar et al., 2024). The over- arching goal is to build models that operate across any human task and any human imagery while maintaining highest output fidelity. In this work, we present SAPIENS2, which advances this objec- tive along all three axes-task, image, and fidelity.
Any human task. Sapiens primarily relied on MAE (He al., 2022) pretraining, form of masked image modeling (MIM) (Hondru et al., 2025). MIM preserves signal and spatial details by opti- mizing reconstruction and thus primarily learns by compression (Zhang al., 2022). Unlike lan- guage where tokens are discrete and largely self-semantic and masked modeling has become a default-visual semanties are denser, context-dependent and under-comstraanee by pixel prediction alone; consequently, MIM features often require moderate-to -hihh supervision to express semantics reliably. In contrast, contrastive learning (CL. (Chen et al., 2020a) injects semantics by enforcing instance-level invariances using positives and negatives (Chen et al. (2020b), Chen et al. (2021)), yet its global invariance objectives tend to underperform on dense prediction, where fine spatial detail and photometric fidelity matter. This gap has motivated hybrids that combine global CL and MIM such as iBOT''s masked student-teacher matching (Zhou et al., 2021) and successors such as DI- NOv2 (Oquab et al., 2023) and v-JEPA (Bardes al., 2024). While these approaches narrow the gap, performance at high resolution remains mixed and can exhibit representation drift: aggressive invariances (notably appearance augs.) decouple teacher and student from the true observations. eroding cues-such as color-that are critical for human-centric dense tasks (e.g. photorealistic avatar creation). SAPIENS2 addresses these limitations by coupling reconstruction objective with contrastive objectives, anchoring features in pixel space (Huang et al., 2023) while organizing them semantically. The result is a general-purposs representation that transfers across zero-shot, few- shot (Song et al., 2023), and fully supervised regimes and a broad spectrum of human-centric tasks.
Any human image Generalization scales with data and model capacity. During pretraining, we curate IB high-quality human images from web-scale corpus via multi-stage filtering. The col- lection spans diverse ages, ethnicities, backgrounds, and real-world conditions, subject to sin- gle constraint: each image contains at least one prominent person. Beyond this human-centri,
Correspondence rawal kemett.... com

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Published as a conference paper at ICLR 2026
Image
Sapiens-18
Sapiens2-18 (Ours)
DIAD NU
OIDD A1
GIAD
Figure 1: SAPTENS2 for dense-prediction tasks. We compare IB models from both generations segmen- tation, depth, and normals. Sapiens2 improves Sapiens with stronger generalization and sharper segmen- lation of rare classes (lips, longue, earrings). achieving pixel-accurate hair segmentation. geometric tasks (depth, normals), it captures subtler facial, clothing, and hair details all without task-specific architectures.
requirement, we use no task labels and inject no human-specific priors during pretraining. For post-training, target fundamental human tasks-pose -ose estimation (Zheng et al.. 2023). body-part segmentation Thisanke et al. (2023), surface-normal (Bac & Davison, 2024), pointmap (per-pixel XYZ) (Wang et al., 2024) and albedo estimation (Ran al., 2024). Relative to Khirodkar et al. (2024), we scale task-specific supervision by 10x, typically on the order of 1M labels per task, and improve synthetic assets with more detailed geometry and photorealism. On the model axis, our largest variant has 5B parameters, accompanied by 0.4B, 0.8B, and 1B models for different compute settings and broader use. At a native resolution 1K, our largest model achieves among the highest FLOPs reported for vision transformers. Fig. 1 showcases improvements over Sapiens for segmentation, depth and normals. Our models segment tiny accessories such as chains and car- rings, and separate teeth and gums with pixel accuracy. Additionally, the predicted normals better capture facial wrinkles and hair details. Our evaluations show that learning at scale yields strong generalization across unconstrained human images and challenging in-the-wild conditions.
Highest fidelity. Prediction fidelity scales with the number of visual tokens model processes, which in turn grows with input resolution (Zhao et al., 2018). Beyond standard 1K backbones (Khirodkar et al., 2024), we introduce 4K backbone pretrained and post-trained for dense prediction, with task heads that decode to 2K resolution across tasks. make 4K tractable, we adopt a hierarchi- cal design (Li et al., 2022): an initial stack of windowed self-attention layers operates locally to capture texture and fine boundaries, from each window we pool a summary token and then apply global self-attention- siieentionmmirrrinng our 1K models to fuse long-range context. This layout is natu- rally compatible with MAE-style pretraining: after the local stage, masked tokens can be dropped so that information does not flow across masked regions, avoiding the leakage that convolutional backbones typically require masked convolutions to prevent (Gao et al., 2022). We additionally incorporate targeted efficiency and stability upgrades- RMMSoom in place of LayerNorm (Meta, 2025), grouped-qu -uery attention for higher throughput (Ainslie et al., 2023). QK-Norm for robust high-resolution training (Henry et al., 2020) and employ pixel-shuffle (Shi et al., 2016) decoder
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Query
Sapiens
Sapiens2 (Ours)
Query
Sapiens
Sapiens2 (Ours)
Figure 2: k-NN comparison using [CLS] token. SAPIENS2 learns more discriminative, juman-semanne feature space-grouping visually similar concepts and improving retrieval performance high resolution.
for sub-pixel reasoning. Together, these choices fully exploit our high-resolution setting while keep- ing memory in check.
We extensively evaluate SAPIENS2 across various tasks and benchmarks. Figure 2 qualitatively visualizes nearest neighbors retrieved using [CLS] tokens from IK-resolution Sapiens and SAPI- ENS2. Our contrastive pretraining yields feature space that captures human semantics and returns plausible neighbors. Figure 3 further shows that, without any supervision, our model produces human-centric attention maps. Overall, our contributions summarized as follows.
SAPIENS2 is family of transformers (0.4B-5B parameters) pretrained on billion high- quality human images. Our models support 1K native resolution and 4K hierarchical reso- lution and are designed for high-resolution dense predictions.
We use masked reconstruction with contrastive objectives to learn features that generalize in zero-shot settings on human tasks while preserving fine details in dense predictions.
We fine-tune with high-quality annotations for pose, part segmentation, pointmaps, nor- mals, and albedo, achieving state-of-the performance across benchmarks.
RELATED WORK
Self-Supervised Learning. Recent breakthroughs in self-supervised learning at scale fall into two families: (1) Masked Image Modeling (MIM) and (2) Contrastive Learning (CL). MIM follows masked language modeling in NLP, but unlike language- where tokens are self-semantic-image patches are context-dependent. Visual representations are thus denser and more ambiguous. MIM objectives are commonly viewed as a form of compression (Zhang et al., 2022) of the input tokens. Among popular approaches, BEiT (Bao et al., 2021) uses dVAE tokenizer to discretize image patches and trains the model to predict the codebook indices of masked patches, while MAE (He et al., 2022) masks large fraction of patches (75%) and reconstructs the missing pixels directly. Numerous studies adopt this paradigm for pretraining- e.g. U-MAE, CAE, SiamMAE, MR-MAR MAAE, and Sapiens (Khirodkar et al., 2024). Representative methods in CL include BYOL (Grill et al., 2020), SimCLRv2 (Chen et al., 2020b), MoCov3 (Chen et al., 2021), and DINO (Caron et al., 2021). Given their complementarity. combining the objectives is natural; for instance, iBOT (Zhou et al., 2021) combines MIM with CL-style self-distillation, aligning student and teacher features via the masked objective rather than reconstructing pixels or codewords, consistent with JEPA (Assran et al., 2023) and v-JEPA2 (Assran et al., 2025). DINOv2 (Oquab et al., 2023) adopts the iBOT objective their primary pretraining strategy. DINOv3 (Siméoni al., 2025) further scales this approach with improved training recipes. However, latent-space objectives risk abstract drift: the representations are not anchored to observations (images or sentences), inducing lossy compression and discarding cues (e.g. color) critical for dense prediction. In Sapiens2, we combine the image- anchored MAE objective with the semantic CL. objective. Prior work such as CMAE (Huang al., 2023) explores this combination but evaluates primarily classification. In contrast, we study unified objective at billion-scale across multiple human-centric tasks.
Hluman-Centril Vision Models. Many recent works focus on building models for human-centric vision. These models often outperform general models of similar scale on nulann-relate tasks. For instance, HAP (Yuan et al., 2023) uses 2D keypoints to guide the mask sampling process during masked image modeling, encouraging the model to focus on body structure information. Geoman (Kim et al., 2025a) uses an image-to-video diffusion model for geometry estimation.
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Published as a conference paper at ICLR 2026
Figure 3: Human-centric attention, Visualization of [CLS)]-token self-attention across heads in the final layer.
HCMoCo (Hong et al., 2022) and PBoP (Meng et al., 2024) employ multiple encoders ex- ploit multimodal human body consistency through a hierarchical contrastive learning framework. SOLIDER (Chen et al., 2023) introduces human semantic classification loss to inject semantic information into the learned features. LiftedCL (Chen et al., 2022) incorporates an adversarial loss to supervise the lifted 3D skeletons, explicitly embedding 3D human structure information for human-centric pretraining. SapiensID (Kim et al., 2025b) trains model specifically for person re-identification. In contrast to these approaches, Sapiens2 does not inject any explicit human priors beyond the data itself during pretraining. This truly inductive prior-free approach enables scaling to millions of images and model sizes without introducing handcrafted human-centric biases.
Vision Transformers at Scale. Although the largest vision backbones remain order of magni- tude smaller than language models (Lu et al., 2024), the field is scaling rapidly as both data and model sizes grow. To clarify the landscape, we position prior works along three axes: parameters, resolution, and data. Amongst notable recent works, the largest vision backbone in the Perception Encoder family (Bolya et al., 2025) has 2B parameters, is trained 448 px resolution, and uses 5.4B samples. DINOv2 (Oquab et al., 2023) scales to 1B parameters at 512 px and is pretrained on 152M images. ViT-22B (Dehghani al., 2023) remains the largest model by parameter count; it is trained at 224 px and is pretrained on 1M images from ImageNet (Russakovsky et al., 2015). Sapiens-2B (Khirodkar et al., 2024), at 1024 px, was the largest human-centric vision backbone, pretrained on 300M human images. In Sapiens2, we scale to 5B parameters and extend the input resolution to 4K, yielding a vision backbone with the largest FLOPs, trained on 1B human images.
PRETRAINING
This section details our pretraining data and methodology, with emphasis on human-centric curation and design choices that preserve output fidelity and strengthen semantic understanding.
3.1 HUMANS-IB IB DATASET
Scale helps only when the data distribution is diverse, balanced, and high quality (Touvron et al., 2023; Radford et al., 2021; Chuang et al., 2025). From a web-scale pool of <4B images, we iso- late human-centric content via a multi-stage filter: bounding box detection, head-pose estimation, aesthetic and realism scoring, CLIP (Radford et al., 2021) features and text-overlay detection. We remove images that fail realism, quality or other checks. From the remainder, we retain instances where at least one person is > 384 pixels on the short side; images may contain multiple people. We deduplicate via perceptual hashing and deep-feature nearest-neighhor pruning, and we cluster visual embeddings followed by selective sampling (Oquab al., 2023) balance content across poses, viewpoints, occlusion, clothing, scene types, and illumination. Thresholds and balance caps calibrated with small human audits. The result is curated, balanced corpus of 1B high-quality human images for pretraining.
3.2 SELF-SUPERVISED LEARNING
Let I denote the training set. We sample an image x ~ I and draw V random augmentations to obtain views {x; Each view is patchified into N tokens indexed by P {1, ....... .N., i.e., x; = [xx" hepP. Let Jee be positional embeddings (Dosovitskiy et al., 2020) and D Pacc. Pas be our transformer encoder, patch decoder and contrastive decoder respectively. Specifically, Pas maps the encoder [CLS] token to K logits.
Masked Image Modeling. For each view i E (l...., VJ. we sample a binary mask m; E [0,1) 1] with masking ratio r. The masked and visible index sets are defined as M, = {p E P m = =1 and Vi = P M, The encoder Penc processes only visible tokens: 2, enn (1x, + J ) We then form a full sequence by scattering 2 back to V. and inserting a learned mask token at M; 2, = scatter (2, V:) U [eMMAs] + efos pe The decoder &uec reconstructs all patches, & Patc(z,) with outputs (*") Following He et al. (2022), targets are normalized X", and
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Published as a conference paper at ICLR 2026
Lmme"
Encoder
Decoder
View1
CLS Decoder
Image
Encoder
CLS Decoder
View2
Figure 4: SAPIENS2 Pretraining. We combine the masked reconstruction loss (Cmme) with global contrastive on [CLS] (Ca). Multiple image views are generated, and coudent-teacher framework matches predicted distributions across views. Lise helps the model learn low-level details (e.g.texture) for high-fidelity dense tasks, while La improves semantic understanding across human images.
the loss averages MSE over masked tokens and views:
MAAE
Σ PEM:
21122
V
M
Contrastive Learning. We adopt a student-teacher scheme based on DINOv3 (Siméoni et al., 2025); the teacher has same architecture (Denes Pets), is non-learnable, and parameters are an EMA of the student. For each view i, the student and teacher [CLS] embeddings and logits are
c [cms)( ci = [CLS] (eee)
(at((c)),
(c))
with soft and q; softmaa((t)). For the V-view (global + local) setting, we form the positive pair set consisting of all cross-view global+-global and global<+local pairs (excluding same-view matches for global crops; local<>local pairs skipped). The contrastive objective averages a seacher-to-stude cross-entropy over these pairs:
La
1
H(g; Pi), (iJ)ES
Σ
H(q.p) = Σ ql log k=1
S
Finally, Fig. shows our pretraining setup for V = 2; for clarity, the figure depicts the global contrastive objective only. We use a joint objective C LMAE + XLeL, combining human-centric low-level fidelity with niwwinvariaant semantics.
NxD
K times
Litees
N
D
MODEL ARCHITECTURE
@
Win-AA
WinSAA
We revise the backbone stably scale to 5B pa- rameters, increase the input resolution from 1K to 4K, and maintain compatibility with sparse masked pretraining. The mid-depth blocks use grouped-query attention (GQA) (Ainslic et al., 2023), while the early and late blocks use stan- dard multi-head self-attention. We replace the feed-forward layers with gated SwiGLU-FFN variants (Shazeer, 2020). For long-schedule stability, we apply QK-Norm (Henry et al., normalzzznn queries and keys before attention and substitute LayerNorm with the parameter-efficiet RMSNorm (Zhang Sennrich, 2019). To scale 4K inputs, we adopt hier- archical attention design (Ryali et al., 2023): given an H x W image with patch size yielding N = (W/pp) tokens, the first K layers apply windowed self-attention to capture local struc- ture. We then downsample the 2D token grid by spatial stride v via [CLS]-guided pooling to ob- tain N/W tokens. Next layers use global attention over this reduced sequence, refer Fig. 5. During pretraining, apply token masking after the local stage, and include brief
Patchify
WinSA
Wi-SAA
Gbbai-SA
Ghbal-S4
Win-SA
WinSAA
Figure 5: Windowed self-attention for 4K resolution.
phase at 2K to sharpen sub aibelixel fidelity on dense tasks without degrading semantics. Finally, we increase decoder outputs KK for base backbones (from 0.5K) and to 2K for 4K backbones.

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Published as a conference paper at ICLR 2026
a) Pose (308 Keypoints)
b) Segmentation (29 Classes)
Synthetic (Pointmap, Normal, Albedo)
Figure Post-Training Annotations. We annotated 100K in-the-wild images with pose (a) and segmentation (b), class vocabulary is also extended to include eyeglasses (in cyan). For pointmap, normal, albedo (c), we improve our synthetic assets to capture finer geometric details and color variations.
5 FOST-TRAINING
We fine-tune the pretrained backbone on five human-centric tasks- masks-pose estimation, body-part seg- mentation, depth, surface normals, and albedo using lightweight task task-specific heads while leaving the backbone unchanged. Relative to Khirodkar et al. (2024), we broaden supervision and refine task objectives.
Pose Estimation. We follow a top-down paradigm to estimate keypoint heatmaps from an input image. Our keypoint topology a 308-keypoint full-body skeleton with dense coverage of the face (243) and hands (40 total), with the remainder spanning torso and lower-body. Unlike Khirodkar (2024), which relied solely on capture-studio annotations, we add in-the-wild supervision (Fig. 6a) by newly annotating 100K high-resolution images from our pretraining corpus with the same vocabulary. This hybrid supervision improves generalization to unconstrained images. Our objective uses MSE over ground-truth heatmaps with OHEM (Chen et al., 201 8) to focus supervision within a large keypoint set Lpose Even
[[(u) H(u)) 22-
Body-Part Segmentation. Our segmentation vocabulary has 29 classes (extended from the previous 28 by adding eyeglasses; Fig. 6b). The vocabulary targets part-specific supervision and precise localization of semantic human body parts. Similar to pose, we increase segmentation supervision to 20K in-the-wild images with segmentation labels. Our objective uses per-pixel weighted cross- entropy combined with Dice loss (Azad et al., 2023) for sharper boundaries.
Pointmap (Depth) Estimation. Rather than relative depth, regress per-pixel 3D pointmap P(u) E R in the camera frame. Since metric scale ambiguous with unknown intrinsics (Yin al., 2023), we predict focal-normalized pointmap P(u) and scalar head s, forming Pu) s Pu)) (Bochkovskii et al., 2024). Supervision is entirely synthetic and uses higher-fidelity assets (hair, eyes, fine facial wrinkles, Fig. 6c). The loss Lpoinimup = Ewen [P((u) (P(u-P(w))lee (2))]]2+ (V)())) (P(l)ll2 where V is finite differences along XY.
Normal Estimation. We predict per-pixel unit normals Ñ(u) E R for human pixels using the same high-fidelity synthetic assets; the decoder multiple PixelShuffle (Aitken et al., 2017) layers for artifact-free upsampling. The loss is defined as: Lnormal Eveo(1 (((u-N(())) + [[[(u) VN(u)]l2
Albedo Estimation. We predict per-pixel diffuse albedo A E 1]3, crucial for relighting (Kim
al., 2024). Training uses high-fidelity synthetic pairs A ( (Fig. 66) and encourages illumination-
invariant recovery of skin tone and clothing. The loss Calbedo = Σnen [l)( A(u) l22 +
(v)( VA(u)l22+ + (lı(A) ((A)ll2, where u((:) is the spatial RGB mean for alignment.
EXPERIMENTS
In this section, we initially outline implementation details, then evaluate pretrained feature general- ization using dense probing and post-train performance across variety of downstream tasks.

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Published as a conference paper at ICLR 2026
Model
Parent-Mode
#Params
FLOPs
Hidden size
Layers
Heads
Sapiens2-0.4B
Sapiens-O.3B
0.398 B
1.260
1024
24
16
Supiens2-0.8B
Sapiens-0.6B
0.818 B
2.592
1280
32
16
Sapiens2-1BB 1B
Sapiens- 1B
1.462 B
4.715 T
1536
40
24
Sapiens2-5B
5.071 B
15.722 T
2432
56
32
Table I: SAPIENS2 architectural details. Broadly, we base the smaller models the first generation and introduce a 5B variant that scales both depth (layers) and width (token embeddings).
6.1 IMPLEMENTATION DETAILS
Sapiens2 is implemented in PyTorch with HF-Accelerate (Gugger et al., 2022). All our models are trained on A100 GPUs using 6 and FSDP for efficiency. We use fused AdamW (Loshchilov & Hutter, 2017) as the optimizer for all experiments, with a brief learning-res rate warmup followed by cosine decay. We pretrain from scratch 1024 768 (1K) and 4096 x 3072 (4K) resolutions. Starting from Sapiens-O.3H 0.6B and 1B, we apply the architectural revisions in Sec. 2 to produce Sapiens2-0.4B. 0.8B and IB. To push the frontier for human-centric vision models, also intro- duce a 5B model that scales both network depth and token embedding dimensions. Sapiens2-5B is the highest-FLOP, vision transformer 15 TFlops. Table summarizes our model configurations at 1K resolution. Finally, we fine-tune the IB 4K model for segmentation and normal estimation.
Evaluation. We construct tpsk-specific test sets to measure fidelity and generalization, and impor- tantly go beyond existing benchmarks in annotation quality. Each set contains challenging in-the- wild samples. For pose, we evaluate on 11K images annotated with 308 keypoints, in contrast the 5K capture-studio images used by SAPIENS. For segmentation, we use similar in-the-wild test of 5K images with 29 classes. For pointmap, normals, and albedo, following Saleh et al. (2025), we evaluate 10K-image test set built from photorealistic assets with higher geometric detail. Please refer to the appendix for additional details.
6.2 PRETRAINING GENERALIZATION: DENSE PROBING
To evaluate zero-shot generalization of the pretrained backbone, we perform dense probing and compare against sfte-of-theear art vision backbones- Sapiens (Khirodkar al., 2024). PE (Bolya et al., 2025). DINOv2 (Oquab et al., 2023), and DINOv3 (Siméoni et al., 2025) across a variety of human tasks. For dense probing, freeze the backbone and lightly train task-specific decoder with identical hyperparameters across all methods. The tasks vary in their demands: for pose es- timation, high-level human semantics aid keypoint localization, whereas for albedo recovery, the backbone must closely capture input appearance. Table 2 reports task-specific metrics across mul- tiple model sizes. Among baselines, DINOv3 strongest for pose and geometric understanding (e.g., pointmaps), owing to its contrastive objective and scale. Sapiens (Khirodkar et al., 2024),
Seg
Pointmap
Normal
Albedo
Model
Params
mAP
mAK
mioti (NR)
mAoc (361
L2
MADP
% 3-22..5
MAB(X10-7
PE-L (Bolya al., 2025)
0.30B
34.8
38.4
42.1
62.3
0.537
74.5
4.22
PE-H (Bolya et al., 2025)
0.63B
50.2
53.8
45.8
65.3
0.529
17.1
76.2
4.14
DINOv2-G (Oquab et al., 2023)
1.148
59.5
63.1
62.7
78.9
0.432
15.0
80.7
3.92
Sapiens BB (Khimdlar et al., 2024)
1.17B
58.2
61.8
61.4
78.2
0.532
15.3
80.1
3.85
Sapiens 28 (Khirodkar et al., 2024)
2,16B
63.4
66.9
65.1
80.6
0.515
14.6
81.4
3.72
DINOv3-R (Siméoni et al., 2025)
78.9
0.492
DINOv3-1. (Siméani (Simmen et al., 2025)
0.348
63.8
66.8
65.5
80.0
0.465
15.6
79.7
3.95
DINOx3-H (Siméoni et al., 2025)
0.88R
67.6
70.4
65.4
81.4
0.448
15.2
80.5
3.86
DINOv3-7B (Siméani al., 2025)
6.71B
68.2
71.6
67.6
83.3
0.398
14.2
82.5
3.48
Sapiems2 0.4B (Ours)
0.39B
65.2
68.2
64.8
79.9
0.471
15.0
80.5
3.96
Sapiens2 0,80 (Ours)
0.828
66.2
69.1
66.9
81.8
0.435
14.4
81.9
3.89
Sapienx2. IB (Ours)
1.46B
68.3
71.4
65.2
82.9
0.428
14.5
81.6
3.64
Sapiens2 (Qurs)
74.7 (46.)
77.4
(45.8)
69.6
(43.0)
83.5 (40.2)
0.358 (-004)
(407)
(41.2)
(-0.6)
Table 2: Dense probing on human tasks. We freeze the backbone and fine-tune a lightweight, task-specific decoder with identical hyperparameters across all methods.
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Model
Input Size
mAP
mÀR (%)
Model
mloli (%)
måce (%)
ViTPuset-L, TPAM123
256 192
47.8
53.6
NiTPoset-H, TPAM123
256 192
48.3
54.1
SegFormer. Neurips2]
45.2
68.3
Mask2Fomerr CVPR22
48.7
71.5
DWPosc-M, ICCV23
256 x 192
60.6
67.4
DWPuse-L, ICCV23
384 x 288
66.5
72.8
Deeplab ECCV18
42.8
66.9
HRRNaV2=-CR
47.3
70.2
RIMW-L, axxiv23
384 288
70.1
75.9
Supiens IB*, BCCV24
53.8
74.7
RTMW-X, arxiv23
384 x 288
70.2
76.1
Supiens BCCV24
1024 x 768
76.8
79.3
Sapiens-2B*. ECCV24
1024 768
78.3
82.1
Sapiens2-0..e 0.4B (Ours)
79.5 (-1.))
90.9 (-177)
Sapiens2-O.8B (Ours)
80.6 (422.)
(-12
Sapiems2-0.4t 2-0.4B (Ours)
1024 x 768
76.9
81,3
Sapiens2: IB (Ours)
81.7
9116
Sapiens2-0.8B (Ours)
1024 768
(+14)
83.1
Sapiens2. IB-4K (Ours)
(-188)
Sapiens2-1B (Ours)
1024 x 768
80.4 (42.1)
84.0 (+1.9)
Sapiens2-SB (Ours)
82.5 (+24.7)
91.1 (-13.))
Sapiens2-5B (Ours)
1024 x 768
82.3 (+4.0)
85.3 (+3.2)
Table 4: Segmentation on 5K test. All methods have the same train set. "De- notes v/ open-soureed models.
Table 3: Pose estimation 11K test. Flip test used,
detections. *Denotes v/ open- sourced models.
due to its musked-autoencodder pretraining, has limited semantic understanding but retains low-level appearance cues useful for albedo estimation. With our combined pretraining objective, Sapiens2 outperforms baselines at comparable model sizes, and our largest model, Sapiens2-5B, surpasses all baselines across every task.
6.3 COMPARISON WITH STATE-OF-THE-ARE METHODS
To understand performance and generalization across human-centric tasks, we compare our models against task-specific state-of-the-art methods in this section. We provide brief summary here and refer to the appendix for detailed analysis.
Pose. We compare Sapiens2 with state-of-the-arr whole-body top-down pose estimators in Table 3. We retrain baselines on our new keypoint set using recommended settings. Our models substantially improve over the first generation; specifically, Sapiens2-0.8P despite its smaller parameter count, outperforms larger models due to architectural improvements and broader supervision. Consistent with scaling laws Kaplan et al. (2020), our results show predictable gains with increased scale. Our largest model, Sapiens2-5B, sets a new state of the art for dense 308-keypoint predictions in-the- wild, achieving 82.3 mAP challenging poses.
Segmentation. Table compares our models to state-of-the-art methods on our segmentation vo- cabulary. For fairness, we train all baselines our training Sapiens2 generalizes strongly to in-the-wild images with high-resolution outputs. Although the input resolution is the same (1K) for Sapiens and Sapiens2, Sapiens2-IB outperforms Sapiens-IB by 27.9% mloU and 16.9% mAcc, owing to in-the-wild supervision and an increased output resolution of 1K (from 0.5K).
Image
Sapiens-1B
Sapiens2-1B-4 (Ours)
Figure Body-part segmentation using our 1B-4K model.
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Distance
Abs. Error
Method
(c
RMSE
(c
Y (e
(c
UniDepth, CVPR24
0.368
0.689
8.34
10.92
5.23
DUS:3R, CVPR24
0.349
0.663
10.11
VGGT, CVPR25
0.217
0.515
3.79
4.96
2.19
MoGe, CVPR25
0.202
0.486
3.21
4.41
1.89
Sapiens2-0.4B (Ours)
0.190
0.466
3.15
4.33
1.76
Sapiens2-0.88 (Ours)
0.186
0.459
3.12
4.26
1.72
Sapiens2-1B (Ours)
0.178
0.478
2.95
4.01
1.66
Sapiens2-5B (Ours)
0.167
0.443
2.85
3.86
1.55
Table 5: Pointmap evaluation in focal-length normalized canonical coordinates on 10K test.
Pointmap. Table 5 compares Sapiens2 with existing pointmap (XYZ) estimation methods such as UniDepth (Piccinelli al., 2024), DUSt3R (Wang et al., 2024), VGGT (Wang et al., 2025a), and MoGe Wang et al. (2025b). This task is more challenging than relative depth estimation, as it re- quires reasoning about camera intrinsics. For fairness, optimize for scale and evaluate all predic- tions in focal-length -ere-llengg--normlizeddd normalized canonical space. Our models outperform all baselines, including MoGe (Wang et al., 2025b), across all model sizes. Fig. 8 qualitatively compares Sapiens2- IB with MoGe, showing that our predicted pointmaps better preserve human-specific geometric details.
Normal. We compare our finetuned normal estimators with current state-of-the-ar monocular meth- ods in Table 6. Our evaluation set consists of whole-bodk scan images captured from random virtual camera viewpoints, with ground-truth normals available at 4K resolution. Our smallest model, Sapiens2-0.4B outperforms existing methods by achieving mean angular error of 8.63%, with 94.76% of human pixels below the 30" threshold. Fig. 9 compares Sapiens2 with the baseline DAViD Saleh et al. (2025) and shows that it captures geometrie details accurately and remains bust under varying lighting conditions.
Albedo. Table 7 reports quantitative albedo results tent improvement with scale; Sapiens2-5B achieves the lowest MAE of 0.012 and highest PSNR of 32.6 dB. Despite training solely on synthetic data, model recovers true skin tone under varying
10K test set. Our models show consis-
Image
MoGe, CVPR 2025
Sapiens2-18 (Ours)
Figure 8: (Top) Pointmap qualitative comparison of Sapiens2-1P IB with MoGe (Wang et al., 2025b), (Bottom)
Depth visualized from the predicted pointmap, along with surface normals novel 3D viewpoints.

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Published as a conference paper at ICLR 2026
Angular Error
% Within
Method
Mean
Median
5
11.25
30
Marigold, CVPR24
18.83
15.27
9.41
39.87
45.21
DSINE, CVPR24
17.24
13.51
11.67
45.62
48.79
Sapiens-1B* BCCV24
13.62
10.11
32.18
69.34
82.14
Sapiens-22 BCCV24
12.38
9.46
37.05
70.54
85.62
DAViD-L, ICCV25
10.73
7.49
42.91
72.16
89.27
Supiens2-0.4B (Ours)
8.63
5.25
49.13
76.89
94.76
Sapiens2-0.8B B.BBBB (Ouns)
8.49
4.75
51.18
77.19
94.81
Supiens2-1B IB (Ours)
7.12
3.75
58.31
81.69
95.77
Supiens2- 1B-4K (Ours)
6.98
3.08
59.07
82.10
95.88
Sapiens2-5B (Ours)
6.73
2.74
62.80
83.06
96.13
Table 6: Normal evaluations on 10K whole-body test set at 4K ground-truth resolution.
Model
MAE
RMSE
PSNR
SSIM
Grad-L1
Supiens2-0.4B
0.01825
0.03257
29.74
0.889
0.00642
Sapiens2-0.8B
0.01602
0.02876
30.83
0.903
0.00624
Sapiens2-IB
0.01224
0.02392
32.43
0.914
0.00612
Sapiens2-5B
0.01191
0.02341
32.61
0.915
0.00610
Table 7: Albedo estimation on 10K test set with ground-truth from synthetic renders.
lighting conditions and generalizes to in-the-wild images (Fig. 10). Unlike diffusion- based meth- ods Liang et al. (2025), model is feedforward and significantly more efficient at inference.
CONCLUSION
SAPIENS2 introduces high-resolution, human-centric models pretrained 1-billion-image dataset. Our models simultancously learn appearance cues and semantics by combining masked reconstruction and contrastive objectives. They consistently outperform general-purpose models on human images and extend to tasks ranging from pose estimation to albedo recovery. SAPIENS2 sets new benchmark for high-fidelity dense predictions and provides robust foundation for applications requiring nuanced, detailed understanding of humans in unconstrained visual contexts.
Image
DAVID, ICCV 2025
Saplens2-18 (Ours)
Image
DAVID, ICCV 2025
Sapiens2-18 (Ours)
Figure 9: Normal prediction. Qualitative comparison of Sapiens2-18 with DAViD (Salch et al., 2025).
DIEEY
Figure 10: Albedo estimation using Sapiens2-1B. Our model effectively encodes low-level details crucial for albedo estimation and generalizes well in to-the-wild images, despite being trained limited synthetic data.
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ACKNOWLEDGMENTS
We gratefully acknowledge the following individuals for their contributions and support: Amaury Aubel, Sofien Bouaziz, Nicholas Dahm, Simon Dong, Lucas Evans, Ish Habib, Kris Kitani, Devansh Kukreja, Junxuan Li, Maxime Oquab, Tero Pikkarainen, Don Pinkus, Kaila Prochaska, Wei Pu, Nir Sopher, Jess Wiese.
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A APPENDIX
A.1 PRETRAINING
A.1.1 IMPLEMENTATION DETAILS
We use the dense-probing evaluations as the final metrics to guide any design decisions during the pretraining stage. For instance, pretrain the SAPIENS2-IB (embed dim 1536, 40 layers, 24 heads, patch size 16, final norm with [CLS]) at 1024x 768. Training uses a joint MAE and contrastive objective: 8-layer MAE decoder (dim 512) with reconstruction, and [CLS] projection head for contrastive learning. Loss weights are MAE: 1.0, CLS: 0.4, KoLeo: 0.04. We adopt multi-view training with global and 4 local crops: global crops random resize-crop in ratio [0.5, 1.0), local crops in [0.2, 0.7]. with standard color/blur/solarize and horizontal flips. Inputs are normalized to ImageNet means/stds. Importantly, we do not use color augmentations on the global views used for masked reconstruction objective.
Optimization uses fused AdamW 1x10-4, (0.,3))((0.,00.55) wd 0.05) with zero-decay for norms, biases, positional and special tokens. We train for 5x10" iters with 10 warmup, cosine decay to 10-7, and global grad-norm clip 5.0. The contrastive teacher EMA is 0.992 (center mo- mentum 0.9); student temperature is 0.1, teacher temperature warms from 0.065 to 0.07 over the first 10s iters. We evaluate every checkpoint for downstream tasks with a frozen encoder and report results using the best checkpoint.
A.1.2 MASKING STRATEGY
Given the high resolution of our backbones. we use mixed blockwise/patchwise masking (blockwise prob 0.4) with a 75% mask ratio patch size 16, refer Fig. x024x768 (64x48=3072 patches), this masks ~ 2304 patches image, yielding coarse occlusions that regularize MAE while leaving sufficient context for contrastive learning.
Blockwise Masking
Patchwise Masking
We randomly mix blockwise and patchwise masking provide coarse occlusions. For MAE pre- training high resolution (1024), 75% mask ratio. Each sample represents (ground-truth image, masked input, reconstruction).
Figure 11:
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Figure 12: visualize encoder features using PCA major components) with different colors. We foreground masking to extract patch features for human pixels. Sapiens2 features capture texture and color information as well as showcase human semantics.
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A.2 POSE ESTIMATION
We evaluate Sapiens2 using ground-truth bounding boxes on our in-the-wild test set for 308 key- points. We fine-tune top-down pose estimator initialized from pretrained checkpoint with the [CLS] token disabled so the encoder outputs a feature map. The head is a heatmap decoder with in-channels 1536 and out-channels 308 (keypoints). It uses deconvolution stages (kernel 4, stride 2) for 4x upsampling, followed by 1x 1 convolutions with channels (768, 768, 512) and final 1x1 projection 308 heatmaps. We adopt UDP heatmaps (stride 4, a= and optimize weighted MSE loss. At test time, we enable flip testing with heatmap fusion.
Optimization uses AdamW 5x10 (2)=(99, 0.999). weight decay 0.1) with layer-wish learning-rate decay and zero weight decay for biases, positional embeddings, relative position biases, and norms. We clip gradients to a global & norm of 1.0. The schedule warms up linearly for 500 iterations (start factor 10-3), then follows polynomial decay (power: 1.0) for the remainder. In addition to the main table, we provide fine-grained evaluations in Table 8, which compares Sapiens2 with Sapiens.
Foot
Face
Left Hand
Right Hand
Whole Body
Model
AP
AR
AP
AR
AR
AP
AR.
AP
AR
Sapiens 0.33 0.3B
72.1
77.6
82.4
86.7
66.8
72.9
67.3
73.2
70.5
77.0
Sapiens-O.66 0.6B
78.9
74.5
72.8
78.6
Sapiens-
75.0
80.1
85.1
88.6
69.7
75.3
70.2
75.7
74.1
79.4
Sapiens 2B
76.0
81.0
86.0
89.2
70.9
76.4
71.3
76.8
75.3
80.4
Sapiens2-O..
78.4
82.0
86.2
89.5
75.1
79.0
75.6
79.4
76.9
81.3
Sapiens2-0.88
80.1
83.4
87.6
90.4
76.8
80.3
77.2
80.7
79.4
83.1
Sapiens2-IB
81.0
84.1
88.3
90.9
81.0
78.0
81.3
80.4
84.0
Sapicns2-5B
82.6
85.3
89.7
91.8
79.2
82.4
79.6
82.7
82.3
85.3
Table 8: Pose estimation results on 10K test set (K=308). Flip test is used.
Figure 13: In addition to wn-the-wild annotations we also use capture-studio 3D triangulated ground-truth -ruth 308 keypoints for finetuning Sapiens2.
18

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Published as a conference paper at ICLR 2026
IELOOIE
Figure 14: Top-down 308 keypoint predictions using Sapiens2-11 IB model on in-the-wild images.
19

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Published as a conference paper at ICLR 2026
A.3 BODY- PART SEGMENTATION
Figure 15: Body-part segmentation (29 classes) using Sapiens2- 1B real-world images.
20

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Published as a conference paper at ICLR 2026
A.4 POINTMAP ESTIMATION
Figure 16: Pointmap using Sapiens2-1B 1B. For each image, visualize the absolute depth derived from the predicted XYZ. pointmap as a heatmap and surface normals computed from depth.
21

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Published as a conference paper at ICLR 2026
Figure 17: Pointmap using Sapiens2-IB. IB. For each image, we visualize the absolute depth derived from the predicted XYZ pointmap heatmap and surface normals computed from depth.
22

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Published as a conference paper at ICLR 2026
A.5 NORMAL ESTIMATION
Figure 18: Surface normal prediction using Sapiens2- IB.
23

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Published as a conference paper at ICLR 2026
A.6 ALBEDO ESTIMATION
Figure 19: Albedo (base color) prediction using Sapiens2-1D IB 1024 768 resolution.
24


Reviews:
[{"review_id": "uDFRcReBoM", "reviewer": "Reviewer_my7v", "summary": "The paper introduces Sapiens2, a new family of Vision Transformer models ranging in size from 0.4 billion to 5 billion parameters. This model family constitutes the second generation of the Sapiens line (ECCV 2024), maintaining the original goal of creating versatile foundation models centered on inputs/outputs depicting humans. The core focus is on achieving strong generalization, versatility, and high-fidelity outputs.\n\nThe primary differences from the original Sapiens models lie in the larger scale of the training data, the pretraining paradigm (hybrid instead of MAE), and the supported resolution (4K). Specifically, Sapiens2 models are pretrained on a dataset of 750 million human-centric images, isolated and curated from an initial web-scale pool of approximately 4 billion images. The pretraining employs a hybrid self-supervised paradigm that combines Masked Auto-Encoding (MAE) with semantic contrastive learning, a technique previously explored by methods like CMAE (Huang et al., TPAMI 2022) for image classification. \n\nThe models are evaluated across five human-centric tasks: pose estimation, body-part segmentation, depth estimation, surface normals estimation, and albedo estimation. Fine-tuning for each task involves adding and training a lightweight task-specific head while keeping the large backbone frozen.", "questions": "1. Are the authors planning to publicly release the curated 750M image dataset, the source code for the pretraining and fine-tuning pipelines, and/or the trained Sapiens2 model weights?\n\n2. Ablation on Pretraining: Could the authors provide an ablation comparing the hybrid self-supervised objective (MAE + Contrastive) against the individual components (MAE-only and Contrastive-only) to justify the added complexity and quantify the specific performance gain attributed to the combined loss?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. State-of-the-Art Performance: The models demonstrate state-of-the-art results across all five evaluated tasks, showcasing the efficacy of the scaling and pretraining approach.\n2.  The evaluation is comprehensive, covering five complex, pixel-level tasks with strong quantitative results and compelling qualitative examples, effectively demonstrating the models' high-fidelity capabilities.\n3. The paper is well written and well structured \n4. Contribution of high resolution (up to 4K) specialized foundation models focused on complex human analysis (conditional on release).", "weaknesses": "1. Limited Fundamental Novelty: the primary advances appear to be an increase in scale (data and backbone size) and the application of an existing hybrid self-supervised paradigm (MAE + contrastive learning, previously seen in classification) to a new domain (human-centric generation). The paper does not introduce novel fundamental insights or methodological breakthroughs in transformer architecture or pretraining theory.\n2. It is currently unclear whether the dataset, code, or the trained model checkpoints will be publicly released. Given that the main contribution of the paper resides in the achieved performance and the massive scale of the trained model family, the lack of public availability would severely limit the reproducibility, impact, and overall contribution to the research community.", "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": "SGn1TsicYm", "reviewer": "Reviewer_mhqq", "summary": "This paper introduces Sapiens2, a family of high-resolution vision Transformer models ranging from 0.4B to 5B parameters designed for human-centric vision tasks. It aims to achieve stronger generalization, task versatility, and high-fidelity output. Building upon its predecessor Sapiens, Sapiens2 undergoes comprehensive upgrades across three dimensions: pre-training, data, and architecture.\n\nUnified Pre-training Objective: It combines Masked Image Modeling (MAE) with Self-Distilled Contrastive Learning (CL). This approach preserves pixel-level details beneficial for dense prediction tasks while learning high-level semantics beneficial for zero-shot/few-shot generalization. The joint objective avoids the limitations of pure MAE which lacks semantics and pure CL which neglects details.\n\nLarge-Scale, High-Quality Human-Centric Data: The Humans-750M dataset is constructed containing 750 million high-quality human images filtered through multiple stages. It covers diverse populations, poses, scenes, and lighting conditions. The only requirement is that each image contains at least one prominent person, with no other manual priors.\n\nHigh-Resolution Scalable Architecture: The model supports native 1K resolution and pioneers a 4K resolution hierarchical model achieved through window attention plus global attention. It integrates cutting-edge techniques including RMSNorm, Grouped-Query Attention (GQA), QK-Norm, SwiGLU FFN, and a PixelShuffle decoder to enhance training stability and efficiency. The pre-training output resolution reaches 2K, significantly improving detail representation for dense predictions.\n\nExtensive Downstream Task Coverage and SOTA Performance: After pre-training, the model achieves state-of-the-art results across five dense prediction tasks including pose estimation, body part segmentation, depth estimation, surface normal estimation, and albedo estimation. It substantially outperforms its predecessor and existing methods, demonstrating significant improvements such as pose estimation mAP improved by +4, body segmentation mIoU improved by +22.3, and normal estimation angular error reduced by 29.2%.", "questions": "1. After collecting such a large volume of human-centric data, what instructive conclusions can be drawn? Building upon a decent pre-trained model, can we continue to scale up the data volume, and what type of additional data would be more effective?\n\n2. When real data is exhausted, considering the use of synthetic data—such as leveraging AIGC models to generate human-centric data—would such generated data be beneficial for this type of discriminative model?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper adopts a simple joint pre-training paradigm of \"masked reconstruction + contrastive learning.\" Although this technique has been widely used in various fields, its application in the human-centric vision domain effectively balances low-level detail and high-level semantic learning, addressing the trade-off between dense prediction and semantic understanding in existing self-supervised methods. Additionally, it scales the Vision Transformer to 4K resolution while supporting 2K output.\n\n2. The experiments are exceptionally solid. Not only does it establish new SOTA results across multiple tasks, but it also demonstrates the model's strong generalization capabilities in zero-shot, few-shot, and fully supervised scenarios through dense probing, ablation studies, and extensive visualizations (e.g., Figures 1, 7–10). The descriptions of data, architecture, and training strategies are detailed and reproducible.\n\n3. A large-scale human-centric dataset was collected and used for training, validating the effectiveness of large-scale data and large models in the human vision domain.\n\n4. The provision of a series of models of different sizes can significantly advance industrial applications.", "weaknesses": "1. Limited generalization in crowded scenes: As mentioned on page 9 of the paper, the model performs excellently on images containing 1-4 prominent people but shows performance degradation in crowded scenes or complex multi-agent interactions. This limits its application in real-world scenarios such as surveillance and sports events. Future work needs to explore more robust multi-person modeling mechanisms.\n\n2. Synthetic data discussion: For geometric and material tasks like point maps, normals, and albedo, training relies entirely on synthetic data. Although results show good generalization (Figure 10), the synthetic-real domain gap remains a potential risk.\n\n3. Data processing pipeline details: The data processing pipeline is important in this paper. While the paper mentions the data processing pipeline, details of each step - such as specific annotation verification and sampling strategies - could be provided. Alternatively, forming a methodology for the community would promote development.", "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": "nLUaJIZkRq", "reviewer": "Reviewer_JoJX", "summary": "The paper presents SAPIENS2, a family of high-resolution ViT backbones (0.4B–5B params) for human-centric vision. \nThe key technical move is a unified pretraining objective that adds a student–teacher contrastive loss on the [CLS] token to MAE reconstruction, aiming to keep low-level fidelity for dense prediction while learning semantic invariances (see Fig. 4 and Sec. 3; the 4K windowed→global hierarchy is sketched in Fig. 5). \n\nThe models are trained on a curated ~750M human-image corpus; post-training heads cover pose (308 kp), 29-class part segmentation, pointmap (XYZ), normals, and albedo. Results on the authors’ curated test sets show improvements over the previous SAPIENS-v1 and strong baselines under a frozen-backbone dense-probe protocol (e.g., Tables 2–6; visuals on pages 8–9).\n\nThe approach integrates ideas known in the literature—MAE for reconstruction and self-distilled/contrastive training à la DINOv2/iBOT—and is consistent with hierarchical ViTs for high-res inputs (Hiera) and stability tweaks like RMSNorm, QK-Norm, GQA. The paper’s novelty is primarily engineering/integration at scale for human-centric dense tasks rather than introducing a new learning paradigm.\n\nSummary of Review:\nThe paper introduces a strong unified pretraining framework combining low-level fidelity (MAE) with semantic alignment ([CLS] distillation) in a 4K-ready hierarchical transformer, showing solid technical design and clear scaling trends. However, the lack of public benchmark validation, detailed ablations, and data transparency limits external interpretability. Overall, it’s a well-executed and promising approach but still short of full maturity, justifying a rating of 6 for solid technical depth with room for broader validation.", "questions": "1) External benchmarks: Can you report COCO-WholeBody results (with 308→133 mapping) and at least one public normals/segmentation benchmark? \n2) More ablations: What are the per-component contributions of RMSNorm / QK-Norm / GQA / SwiGLU and the masking point in 4K?\n3) Data disclosures: Confirm the 750M pretraining size, list high-level source categories, and describe license vetting, deduping, and opt-out mechanisms.\n4) FLOPs accounting: Please give per-image FLOPs at 1K and 4K, the attention configuration, and compare to DINOv2-G and ViT-22B.\n5) Augmentations: How sensitive are albedo/normal metrics to color jitter/solarize on global vs local crops?\n\nMinor:\nIn Figure 4, I think it should be “L_mae helps the model to learn…” and not “L_mae helps the model learns …”.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) Unified pretraining that explicitly marries low-level fidelity (MAE) with semantic alignment ([CLS] distillation); design aligns with robust SSL practice (DINOv2/iBOT/JEPA). \n2) 4K-ready hierarchical transformer with practical stability/throughput tweaks (RMSNorm, QK-Norm, GQA) consistent with the literature.\n3) Fair frozen-probe protocol across backbones; strong internal scaling trends (0.4B→5B) across pose/seg/geometry (Tables 2–6; pages 8–9).", "weaknesses": "1) Public benchmarks missing. Add results on community standards (e.g., COCO-WholeBody 133-kp, plus any public human-part segmentation/normal datasets) to anchor gains. \n\n2) Component ablations. Quantify deltas from RMSNorm vs LayerNorm, QK-Norm, GQA, SwiGLU, and the masking-after-local choice in the 4K pipeline\n\n3) FLOPs claim. Standardize FLOPs reporting (input size, attention type, precision) and compare to published large ViTs (e.g., DINOv2-G, etc) under a common convention.\n\n4) Data governance transparency. Given human images at scale, could be nice to add a data/model card (sources, licenses, demographic coverage, minors handling, opt-out) such that to show overall a general idea regarding the data used.", "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": "lEaNdKWB9b", "reviewer": "Reviewer_iUF3", "summary": "The paper presents SAPIENS2, a significant advancement over the original Sapiens foundation model for human-centric computer vision. The work focuses on increasing generalization, versatility, and output fidelity across a broad spectrum of human-related tasks.The core contributions lie in three areas:\n\n1. Introduction of models ranging from 0.4 billion to a 5 billion parameter variant, trained at native 1K resolution, and new hierarchical 4K models capable of 2K output resolution for dense prediction. The Sapiens2-5B model represents one of the highest-FLOPs vision transformers reported.\n\n2. A novel pretraining objective combining Masked Image Reconstruction\n\n3. Scaling the curated pretraining data to 750 million high-quality human images (HUMANS-750M) and increasing post-training task annotations by 10x for tasks including pose, body-part segmentation, depth, normals, and the newly added pointmap and albedo estimation.", "questions": "How does the performance (especially dense vs. semantic tasks) change as $\\lambda$ is varied?\n\nGiven the \"truly inductive prior-free approach\" and the massive scale of the Sapiens2-5B model, what is its performance on general computer vision benchmarks that do not involve humans (e.g., COCO object detection, ImageNet linear probing, or ADE20K segmentation)? This would help contextualize Sapiens2's feature learning against general foundation models like DINOv2 and M-ViT at comparable scales.\n\n\nThe paper mentions several architectural stability features (GQA, QK-Norm, RMSNorm, SwiGLU). Which of these, or combination thereof, were most critical for the stable training of the largest 5B parameter, 4K input hierarchical models? Was the training of the largest variant notably more sensitive to hyperparameter choices compared to the smaller models?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper demonstrates compelling gains. The increase in performance over the first generation (e.g., +22.3 mIoU on body-part segmentation and +4 mAP on pose estimation) is substantial and justifies the scale and methodological changes. Crucially, the dense probing results (Table 2) confirm that the pretrained features themselves are vastly superior for human tasks, showcasing strong zero-shot generalization. The Sapiens2-5B model setting a new SOTA with 82.3 mAP on dense 308-keypoint in-the-wild predictions is a major result.\n\n2. The core technical argument—that pure MIM lacks semantic understanding, while CL erodes fine-grained photometric detail—is sound, particularly for high-fidelity human-centric dense tasks (like avatar creation or albedo estimation).\n\n3. The decision to avoid injecting handcrafted human-specific priors (beyond data selection) during pretraining is admirable. This \"truly inductive prior-free approach\" demonstrates that raw scale and a general-purpose objective can, in this domain, outperform many hand-engineered human-centric models", "weaknesses": "No significant weaknesses.", "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)