Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation
Authors: Kang Liao
Size Wu
Zhonghua Wu
Linyi Jin
Chao Wang
Yikai Wang
Fei Wang
Wei Li
Chen Change Loy
Venue: ICLR 2026 Poster (2026)
Keywords: Unified Multimodal Model
Spatial Intelligence
Controllable Generation
Camera Calibration
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We make the first attempt to unify camera-centric understanding and generation in a cohesive multimodal framework.

Abstract: Camera-centric understanding and generation are two cornerstones of spatial intelligence, yet they are typically studied in isolation. We present Puffin, a unified camera-centric multimodal model that extends spatial awareness along the camera dimension. Puffin integrates language regression and diffusion-based generation to interpret and create scenes from arbitrary viewpoints. To bridge the modality gap between cameras and vision-language, we introduce a novel paradigm that treats camera as language, enabling thinking with camera. This guides the model to align spatially grounded visual cues with photographic terminology while reasoning across geometric context. Puffin is trained on Puffin-4M, a large-scale dataset of 4 million vision-language-camera triplets. We incorporate both global camera parameters and pixel-wise camera maps, yielding flexible and reliable spatial generation. Experiments demonstrate Puffin’s superior performance over specialized models for camera-centric generation and understanding. With instruction tuning, Puffin generalizes to diverse cross-view tasks such as spatial imagination, world exploration, and photography guidance. We will release the code, models, dataset pipeline, and benchmark to advance multimodal spatial intelligence research.

Published as a conference paper at ICLR 2026
THINKING WITH CAMERA: A UNIFIED MULTIMODAL MODEL FOR CAMERA-CENTRIC UNDERSTANDING AND GENERATION
Kang Liao!
Size Wu
Zhonghua Wu?
Linyi Jin
Chao Wang
Yikai Wang'
Fei Wang*
Wei Wei
Chen Change Loy'
S--ab, Nanyang Technological University
Sense'Time Research
*University of Michigan
*Max-Planck Institute for Informatics
(8) Camera Controllable Generation
(b) Camera Understanding
Horizon Lines:
Ground Truth
UVP
CooCallo
Puffin (Ours)
Prompt: Civen scare descrppion and comeaparding camera
(c) Understanding Generation Thinking
parameters, merge them mo coherent promp and generate an
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eccurate visua higi lights cuis.
Caption:
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<think> alght
form
wal suggwods
(Roll) The image exhiil a lagu abockio Dulch eight, as avidenced by the iited perpeeivve whore the toe and rootinn ard not algned with
horizuntal place, suggesting algokFooot rolation
(Pitch)
the occopis subiwwntl portinn of frane, with fre moddow postloned lomer amphassing
crlantation Indicates
vertical crentation <thines>
Genertted
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Intaal Viww
Gonerated Views
Intiial View
Downstrean Application
30 Roconst uution Vieuuit
World Exploration
Figure 1: Illustration of the versatile capabiliiies of our Puffin model. It unifies camera-centric generation (a) and understanding (b), supports the thinking mode (c), and enables diverse applications (d).
Corresponding authors

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Published as a conference paper at ICLR 2026
ABSTRACT
Camera-centric understanding and generation are two cornrrstones of spatial in- telligence, yet they are typically studied in isolation. We present Puffin, unified camera-centric multimodal model that extends spatial awareness along the camera dimension. Puffin integrates language regression and diffusion-based generation to interpret and create scenes from arbitrary viewpoints. To bridge the modality gap between cameras and lisionlanguage, we introduce novel paradigm that treats camera as language, enabling thinking with camera. This guides the model to align spatially grounded visual cues with photographic terminology while reasoning across geometric context. Puffin is trained on Puffin-4M, a large-scale dataset of 4 million Vision-aagguage-cammea triplets. We incorporate both global camera param- eters and pixel-wise camera maps, yielding flexible and reliable spatial generation. Experiments demonstrate Puffin's superior performance over specialized models for camera-centric generation and understanding. With our designed instruction tuning, Puffin generalizes to diverse cross-view tasks such as spatial imagination, world exploration, and photography guidance. To advance multimodal spatial intel- ligence research, we release the code, models, dataset pipeline, and benchmark at https://kanqliiet wtts:s2..gtttttt gwthtb..hh.......g.. pectsp/seetsspppst
INTRODUCTION
For machines, cameras serve as the primary interface the physical world, enabling spatial intelli- gence that underlies applications such as robotics, AR/VR, and autonomous driving. In general, two principal camera-centric objectives work tandem to enable machines to perceive and interact with their spatial context. On the one hand, understanding the camera geometry from images (Pollefeys) ctt al.) 1999 Veicht et al. 2024: Zhang et al., 2024 Lin et al., (205c)), namely how the 3D world is projected onto the 2D image plane, lays the foundation for machines to recover spatial structure and navigate complex environments. On the other hand, by modulating intrinsic and extrinsic parameters, cameras encode spatial relationships and offer flexible control over spatial content generation (Bernal) Berdun et al.] 2025 He et al.] [024] Ren et al.) [025] Ball et al. (2025). which simulates how the world appears from any viewpoint orientation. To date, these two perspectives have been commonly treated as isolated problems and independently explored by the research community.
In this work, we make the first attempt unify camera-centric understanding and generation in cohesive framework. Motivated by recent progress in unified understanding and generation with large multimodal models (LMMs) (Team] 2024, Wu al.) 2025d: Pan al.] 2025 Wu al. 2025b), we extend this paradigm to the spatial domain, where camera geometry plays central role. However, unlike language or images, camera parameters are abstract and non-intuitive: they describe field-of-view (FoV), orientation, or perspective in numerical form rather than semantic content. This discrepancy introduces a modality gap when integrating cameras into LMMs. For instance, when users specifyy "20" roll" or "35mm lens" for controllable generation, existing models often ignore or misinterpret such cues, pursuing semantic alignment while neglecting precise spatial control. Similarly, current LMMs tend to collapse geometric details into coarse representations when understanding camera information, leading to spatially inconsistent outputs. As a result, naïvely extending LMMs cannot resolve conflicts between modalities, producing suboptimal performance in both tasks.
To address this challenge, we introduce Puffin, unified multimodal framework that interprets cameras as a first-class modality. Puffin combines autoregressive and diffusion modeling to jointly perform camera-centric understanding and generation") Instead of treating camera parameters as auxiliary labels, Puffin introduces the notion of thinking with camera, aligning spatially grounded visual cues with professional photographic terminology while reasoning over geometric context. This design provides a shared chain-of-thee -f-thought across multimodal tasks, enabling spatially consistent understanding and controllably aligned generation.
We mainly focus single-view calibration and text-to-image controllable generation, but Puffin can be flexibly extended to cross-view understanding and generation via designed instruction tuning (see Figure
2

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Published as a conference paper at ICLR 2026
Understanding
Generation
Image
Difluvion
000 Wage Tokers
DENEEENO 000
Tokers
(Diurod)
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(Conthuouu oui
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Ted Tokkntrrr
Camera Raiameter losemzar
Camera Map Tokenita
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Goverale
Aiptt walla
drou actyender
deese
Sprtal (nagietor)
Por cnperraoon and cannc
and to uaneey of'
Image
Pasamdlers
Iriiil
Figure 2: Overview of the proposed Puffin. It jointly learns the camera-centric understanding and generation tasks in unified multimodal framework. The elements bounded with dotted boundaries represent the cross-view understanding and generation during instruction tuning, such as spatial imagination and world exploration.
To support this framework, we construct Puffin-4M, a large-scale dataset of million vision-language- camera triplets. Puffin-4M includes single- view images with precise camera parameters, descriptive captions, pixel-wise camera maps, and spatial reasoning annotations across diverse indoor and outdoor scenarios. Beyond single views, it also incorporates -iew and aesthetic images, making it versatile benchmark for both understanding and generation tasks.
Experimental results show Puffin outperforms specialized models for camera-centric understanding or generation, and can be adapted to diverse downstream applications. We illustrate the versatile capabilities of Puffin model in Figure In each generated image (a), the target camera is marked at the bottom left, and the horizon lines are visualized from the estimated camera parameters (b). For world exploration (d), we visualize 3D reconstruction results derived from the initial and generated views. Our main contributions are threefold:
We make the first attempt to seamlessly integrate camera geometry into unified multimodal model, introducing a camera-centric framework to advance multimodal spatial intelligence.
We propose thinking with camera, novel mechanism that guides the model to align spatially grounded visual cues with photographic terminology, bridging the modality gap between camera and
and enabling effective spatial reasoning.
We construct Puffin-MM, large-scale dataset of 4M vision-language-cmmen triplets span- ning diverse indoor and outdoor scenes, and establish a comprehensive benchmark for evaluating camera-centric multimodal models.
2
UNIFIED MULTIMODAL MODEL
Puffin, as illustrated in Figure 2, unifies camera-centric understanding and generation within multimodal paradigm. For understanding, we introduce a geometry- vision encoder to large language model (LLM) to retain rich geometric features and enhance the model's 's capacity for spatial analysis. For generation, a connector module learns to map the hidden states of the L.LM (via a set of learnable queries) into conditioning signals that can be interpreted by the diffusion model. To facilitate the integration of camera geometry, apart from the discrete camera tokens derived from numerical camera parameters, introduce continuous camera latent obtained from pixel-wise camera maps, allowing fine-grained spatial control in image generation.
2.1 CAMERA UNDERSTANDING
Definition. In this work, camera understanding is formulated cueestionaansweinng task conditioned on image content. The generated text consists of a concise description or spatial reasoning along with the estimated camera parameters (i.e., roll, pitch, FoV) of the input image. Unlike previous methods

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Published as a conference paper at ICLR 2026
Input
Classical Geametty
Thinking with Camera
<Thinkc
(Roll) The image exhiita small counterbbckwws Dutch angle, avedenced by She sight aX of the road and streat agns which are not perfecty signed with the vertical ave me frame.
(Pitch) The dege nhiu oriendation appresen from the promitent was of the sky and the tip portons the lrws, andiaatng that the canere is angled spwands, captosing more of the vedicst eletmants ther standdurs honzontal vow would.
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signi
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<answer 0.1701, D.4638, 58304tanswer
Gaomaaic Cortedd (fol)
Geooratic Conteot (Pacc)
Geomatric Conteat (Fo))
Latrune
Up vector
Learned Semanties (w/ Confidence)
Small coutfercichuise Dusch anglo
Large thuu
Figure 3: Methods for learning camera geometry. (Left) Previous classical and learning-based methods focused on extracting or learning representations such as geometric structures or semantic features (with confidence). (Right) We introduce the notion of thinking with camera through LMMs. It first decouples the camera parameters across geometric context, establishing connections between spatially grounded visual cues (highlighted in the masked regions) and professional photographic terms. The camera parameters then predicted within the <answer></answe>> tag through this spatial reasoning process <thinl>></hinkk.
that directly estimate the parameters from images, our approach integrates camera geometry within the text and performs next-token prediction in a multimodal sequence modeling paradigm.
Motivation. As illustrated in Figure 3 (left), previous classical and shaming-based methods fo- cus on extracting or learning representations to predict the camera parameters, such as geometric structures (Pautrat et al. 2023) semantic features with confidence estimates (Veicht et al. 2024). However, these representations often emphasize low-/mid-level patterns, limiting their ability to capture a holistic and coherent spatial concept. As a result, existing approaches tend excel in scenarios with rich features but struggle to generalize across diverse visual environments.
Thinking. Instead of focusing on how to learn representation, we propose to interpret the camera as language and introduce the notion of thinking with camera. It guides the LMMs to align spatially grounded visual cues with photographic terminology while reasoning across geometric context. The details of each key element are elaborated below.
Spatially Grounded Visual Cues. The 3D world governed by physical laws, where gravity and human design shape stable spatial regularities that serve as strong perceptual priors. Texture-less regions such as sky, ceilings, floors, or ground surfaces lack local features but encode vertical regularities critical for pitch estimation. Similarly, FoV estimation relies on perceiving spatial composition, including the forgground-background ratio, object scale, and depth distribution. While such properties are difficult to infer from purely visual representations, they are implicitly captured by LMMs as knowledge priors. Thus, we embed these spatially grounded visual cues into our thinking captions, enabling the model perform explicit spatial reasoning about camera geometry.
Professional Photographic Terms. Existing LMMs typically acquire over-abstracted semantics, whereas the detailed numerical values of camera parameters are too fine-grained estimate precisely. As a practical alternative, professional photographic terms (e.g., close-up, tilt-up, Dutch angle) are widely used in annotations and well aligned with LMM knowledge (Liu et al., 2025 Wang
et al. 2025b Lin et al., 2025c). Thus, we leverage them as intermediate supervisory signals to naturally bridge low-/mid camera geometry and high-level multimodal reasoning. These terms, derived as quantized abstractions of camera parameters, are merged with textual scene descriptions, making global spatial arrangements linguistically accessible. The parameter-to-ten mapping can be formulated as f p f, in which the mapping ff is shown in Appendix A2.2 (Table Al).
Geometric Context As shown in Figure 3 (right), we decouple camera parameters across geometric context (roll, pitch, and FoV), which aligns specific spatially grounded visual cues such as sky, fore- ground composition, and object-level depth ordering with each professional photographic terminology. By anchoring numerical attributes to semantically meaningful descriptors, our framework bridges abstract visual features and physically interpretable geometry. The final parameters are predicted through this structured spatial reasoning.
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Published as a conference paper at ICLR 2026
With the above designs, we interpret the camera language by grounding its physical attributes in stable spatial regularities. Numerical parameters are abstracted into professional photographic terms, providing semantic vocabulary aligned with LMMs. Through this mapping, camera geome- try becomes linguistically interpretable, allowing structured spatial reasoning for accurate camera parameter prediction. We visualize more reasoning results in Appendix A (iggure/88)
Choosing a Suitable Vision Encoder. A straightforward approach to camera understanding is to fine-tune existing LMMs that couple vision encoder with an LLM, but this naïve strategy faces two major limitations: (i) vision encoders in LMMs are primarily designed for recognition tasks and thus yield condensed features lacking geometric fidelity, and (ii) language components contain little prior knowledge of spatial perception, reducing adaptability to camera-centre tasks. As a result, such fine-tuning can lead to performance bottlenecks and even underperform pure vision- based methods (see Section (33).. To overcome these issues, we introduce a geometry-aligned vision encoder distilled from both semantic (e.g., CLIP, SigI.PP and vision-centric (e.g., DINO, SAM) teachers (Heinrich et al. 2025), offering versatile features that preserve geometric fidelity while maintaining strong semantic understanding. We then align this encoder with an LLM (Qwen et al. 2024) via progressive unfreezing and joint fine-tuning. This staged optimization stabilizes training and fosters spatial awareness that bridges low-/mid-leve structural cues with high-level linguistic reasoning. The detailed training recipe is provided in Appendix A4
2.2 CAMERA-CONTROLIA CONTROLLABLE GENERATION
Motivation. Unlike image understanding, image generation requires complex cross-modal alignment and the synthesis of graignd visual details. As discussed in Section 2. the detailed numerical values of camera parameters are too specific for current LMMs to interpret effectively, failing to faithfully capture the realistic spatial distribution required for camera-controllable generation.
Thinking. To address this, we design a step-by-step process that integrates visual detail analysis with reasoning. The model first infers the potential visual cues from vanilla captions, and then uses this textual reasoning as a semantic planning stage to guide image generation. For instance, a large pitch value may correspond
expansive sky with clouds in outdoor scenes to pendant lights and uncluttered ceilings indoors. Beyond textual reasoning. numerical camera parameters are translated into professional photographic terms suitable for LMMs, naturally aligning with the reasoning process in camera understanding. We therefore adopt a shared chain-of-thought mechanism between understanding and controllable generation. As shown in Figure (c), given small pitch value and a caption describing a modern interior, our method translates the value into a photographic term (e.g., small tilt-down), imagines salient cues such as a windowsill, and produces more precise spatial simulation than the baseline.
Flexible and Faithful Control. The pipeline of camera-controllabbee generation shown in Figure (right). The key design is to incorporate pixel-wise camera maps continuous latent of camera geometry, apart from the discrete camera tokens derived from numerical parameters. Unlike tokens that capture only global attributes, these dense maps encode local geometric context at each pixel, including orientation and displacement cues (Jin et al., 2023). By converting maps into continuous latent, the diffusion model receives fine-grained spatial priors that preserve global camera settings while adapting to subtle geometric variations, thus offering flexible control of spatial layout and viewpoint. Additionally, we introduce a connector module an adaptive interface between the LLM and the diffusion model, where a set of learnable queries together with text and camera tokens extract and restructure L.LM hidden representations, which are then projected into conditioning signals for generation Pan et al. (2025): Wu et al. (2025c). This design enables semantic and geometric understanding from the LLM to faithfully guide the diffusion model.
2.3 INSTRUCTION TUNING
Although our Puffin focuses on single-view camera calibration and text-to-image controllable gen- eration, it can be flexibly extended to cross-view settings with only minor modifications, such as appending additional tokens and switching prompts according to the target task. As shown in Figure 2 the dotted modules denote cross-view understanding and generation. We explore three tasks: (i) spatial imagination, where the model imagines the scene description of a target view given its camera parameters and an initial view; (ii) world exploration, where the model generates the target view,
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Published as a conference paper at ICLR 2026
Table 1: Evaluation results on camera understanding. We color the best and second best results.
Roll [degroos]
Pitch [dogrees)
FoV [dogrees]
Approch
4
A 1/5/10"1
AUCC 1/5/10"
error
AUC 1/5/10*
DeepCalib Lopez et
2019)
1.41
34.6
65.4
79.4
5.19
11.9
27.8
44.8
11.14
5.6
12.1
22.9
CTRL-C (Lo di 204
0.88
54.5
75.0
84.2
4.80
16.6
33.2
46.5
18.65
2.0
5.8
12.8
MegaDepth
MSCC (Saag el a. AUMA1
0.90
53.1
72.8
82.1
5.73
19.0
33.2
44.3
10.80
6.0
14.6
26.2
ParamNar Um
20725
1.17
43.4
70.7
82.2
3.99
15.4
34.5
53.3
11.01
3.2
10.1
21.3
SVA [
Lochman et al 202
31.9
35.0
36.2
13.6
20.6
24.9
9.4
16.1
21.1
UVP Paotrrre
0.51
69.2
81.6
86.9
4.59
36.2
47.4
10:92
GenCalib ( Vorcht
2h24
0.36
82.6
90.6
94.0
1.94
32.4
53.3
67.5
4,46
13.6
31.7
48.2
Puffin (Ours)
0.32
84,9
93.4
96.2
1.08
47.6
68.2
79.4
2.42
23.9
47.8
64.1
DeepCalih Lopez et
2019)
1.95
24.7
55.4
71.5
3.27
16.3
38.8
58.5
8.07
1.5
8.8
27.2
CTRL-C R1
1.68
32.8
59.1
74.1
2.39
24.6
48.6
65.2
5.64
10.7
25.4
TartanArr
MSCC Song ct al.
3.50
15.0
37.2
57.7
3.48
18.8
38.6
54.3
11.18
4.4
11.8
23,0
ParamNer Jin et
20A6
1.63
34.5
59.2
73.9
3.05
19.4
42.0
60.3
8.21
6.0
16.8
31.6
SVA Lochman
9.48
32.4
39.6
44.1
18.46
21.2
28.8
34.5
43.01
8.8
16.1
21.6
UVP Panral al 20253
0.89
52.1
64.8
71.9
2.48
36.2
48.8
58.6
9.15
15.8
25.8
35.7
GevCalih Vaichi
A024
0.43
71.3
83.8
89.8
1.49
38.2
62.9
76.6
4.90
14.1
M1.4
47.6
Putfin (Ours)
0.40
71.7
86.2
92.1
0.95
$1.0
68.2
79.3
7.48
16.3
28.5
39.0
DeepCalib Lopez
2019)
1.15
44.1
73.9
84.8
4.68
10.8
28.3
49.8
10.93
0.7
13.0
24.0
CTRL-C (Lee cl
1023
1.20
43.5
70.9
82.5
1.94
27.6
54.7
70.2
5.64
9.8
24.6
43.2
MSCC Sony
AT24
1.44
39.6
60.7
72.8
3.02
20.9
41.8
55.7
14.78
3.2
8.3
16.8
LaMAR
ParamNer Unn et al.
2024
0.93
51.7
77.0
86.0
2.15
27.0
52.7
70.2
14.71
2.8
6.8
14.3
Lochinan
8.6
3.4
5.7
2.7
4.1
UVP [Paitral of al:
20z23
0.38
72.7
81.8
85.7
1.34
42.3
59.9
69.4
5.57
15.6
30.6
43.5
GcoCalid (Vichtt et
2024
0.28
86.4
92.5
95.0
0.87
55.0
76.9
86.2
3.03
19.1
41.5
60.0
Puffin (Ours)
0.38
80.6
89.8
93.5
0.71
61.7
78.9
86.4
3.62
17.0
37.3
53,1
DeepCalib (Lopez
2019
1,90
29.3
56.2
71.7
3.71
15.3
36.0
54.9
7.43
9.0
19.4
34,8
Purfin-Uni Und
CTRI,-C (T al 2022
4.69
20.3
35,2
46.7
8.43
10.8
24.6
36.1
11.70
5.3
12.7
23.5
MSCC (Song et
20747
4.40
17.4
34.7
47.9
6.87
13.1
26.3
38.9
9.79
6.8
16.3
29.0
ParamNet Uni
2016
2.11
24.9
53.6
71.5
3.40
16.1
38.7
58.6
6.21
9.4
22.3
39.8
UVP (Pautzal al. 2025)
2.03
32.7
46.4
54.9
9.04
11.4
22.6
32.5
18.80
5.0
12.1
19.9
GevCalih Vaichi 2024
0.92
53.6
73.9
82.6
2.18
28.9
52.5
69.6
5.04
12.4
28.0
45,8
Puffin (Ours)
0.41
78.3
91.0
95.2
0.74
60.2
81,2
90.0
1.21
42.4
70.5
84.3
incorporating an additional yaw parameter to represent cross-view deviations and conditioning on both the target-view camera map and the VAE-encoded initial view (with text descriptions randomly dropped to support both rext-conditioned and text-free generation); and (iii) photographic guidance, where the model suggests camera parameter adjustments from an initial view to achieve images with higher photographic aesthetics, Visualization results are presented in Figure 6
EXPERIMENTS
Datasets and benchmarks that span vision, language, and camera modalities remain scarce in the domain of multimodal spatial intelligence. To address this gap, we introduce Puffin-4M, a large-scale, high-quality dataset comprising 4 million Aision-laguaage-cmmer triplets. The construction of this curated dataset consists of four stages: panoramic data collection and preprocessing, perspective image generation, scene and spatial reasoning captioning, and extensions for cross-view instruction tuning. Details about the constructed dataset and training recipe of our framework are presented in Appendix A3 and Appendix A4 respectively.
3.1 EVALUATIONS ON CAMERA UNDERSTANDING
Settings. Following prior works, we compare our method against a range of learning-based cam- era calibration approaches, including DeepCalib (Lopez et al.) 2019). CTRL-C (Lee al., [021]). MSCC (Song et al. 2024), ParamNet (Jin et al., (023). and Calib (Veicht et al. 2024p. as well as traditional methods such as SVA (Lochman et al.) 2021) and UVP (Pautrat et al. 2023). ). For each image, gravity estimation is evaluated using the angular errors in roll and pitch, while focal length evaluated through the error in vertical FoV. For all metrics, we report both the median error and the Area Under the Recall Curve (AUC) at thresholds of 1 5º, and 10", We conduct evaluations on three common datasets, MegaDepth (Li & Snavely 2018), TartanAir (Wang et al. 2020), and LaMAR (Sarlin et al. 2022). Notably, images from these datasets are primarily captured or simulated in well-structured environments, where buildings, rooms, or trees occupy a substantial portion of the scene. Moreover, the camera parameters in some datasets are limited in distribution; for instance,

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Published as a conference paper at ICLR 2026
GPT-40
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Figure 4: Comparison results controllable generation. We visualize the generated image along with its map (latitude up vector, estimated by (Veicht al. 2024 error map the GT camera map. and the median error. The caption and target camera map are presented the bottom of each comparison.
Table 2: Camera-controllabbee generation evaluation on Puffin-Gen. When evaluating multimodal models, we convert the camera parameters from radians to degrees* or express them using standard photographic terms
Up Vector [degrees]
Latitude [degrees]
Gravity [degrees]
Visual Quality
Approach
mmedia
etTor mmdeia mean mediian
FID
OpenAl
2025
95.92
GPT-4n OpenAT
2725
24.07
22.10
14.67
12.43
27.19
26.32
94.43
Qwen Imuge
Wu el
2025a
23.80
22.73
15.76
13.90
27.75
27.22
83.31
Qwen Quen-Image
Wn
2025a
23.98
22.60
15.92
13.92
27.86
26.45
83.37
Nano Banana
202.5
24.08
23.13
16.66
15.05
28.78
28.22
91.66
Nano Banama
Google Deep-inn
2025
24.65
23.50
15.80
13.98
28.22
26.73
88.02
PreciseCam Bernal-Berdun Herdun ml. 2125
18.66
17.47
12.49
9.99
18.39
15.34
90.91
Puffin (Ours)
11.94
10.12
6.34
6.79
3.43
69.46
TartanAir uses single FoV for all images. To complement these settings, we construct more challenging dataset, Puffin-Und, designed for a comprehensive assessment of camera understanding. This dataset contains 1,000 images spanning diverse camera configurations and scenarios.
Comparison Results. As shown in Table our method outperforms the baselines on MegaDepth and Puffin-Und. and achieves comparable results on TartanAir and LaMAR. Due the fixed image resolution in our training data (512 x 512), we adopt central cropping strategy followed by resizing to rectangular inputs for evaluating non-square images. The vertical FoV then computed from the predicted value and scaled according to the crop ratio. Nevertheless, this procedure may discard semantically valid content and thereby degrade our camera understanding performance, particularly when the aspect ratio deviates substantially from unity, in LaMAR, where Puffin slightly underperforms the state-of-the. of-the-art method (Veicht et al., 2024). While this limitation is orthogonal to current exploration, it could be potentially mitigated in future work by constructing a multi-scale training dataset. We present the horizon lines derived from the predicted camera parameters of different methods in Appendix A5.1 (Figure A9).
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Table 3: Ablation study camera understanding. We evaluate our method with different architectures and the mode of thinking with camera.
Roll [degrees]
[degrees]
FoV [degrees]
Appmach
errVr 1
AUC 1/5/10" 1
error
AUCs 1/5/10"
emre L
AUC 1/5/10"
InterVVL.3 3 (Thu et al.
2025
0.91
53.7
75.5
85.6
1.72
31.9
59.7
76.1
2.96
19.7
43.1
63.5
Qwen2.5 2-22.-VV Bar
2025
0.79
58.8
78.0
86.5
1.61
36.4
62.4
78.0
2.91
19.4
42.5
Vision Encoder Heurehh
2025
0.55
69.0
86.2
92.6
1.00
49.8
74.1
85.9
1.87
28.3
57.9
75.9
Ours
0.47
75.6
89.7
94.6
0.91
54.2
77.5
87.9
1.48
38.0
66.2
81.5
Thinking
78.3
91.0
0.74
60.2
90.0
1.21
70.5
Gererstd imoge
Goner Image
Enirrs
wib Carmerc Map
Errot Map
Capton: A gand Aittrie bulldng faauiing poetted arctes, nbond vasts, and omom mone ookurns, better werm lght shandetior, cxuding an atmogatore reverence paramaters siguiionce
The inago
large bil-up
Camera Map
appareet prespective emphastres
postloeed captire the lossning archer and
the intricate details of the colling, ouggesting
and fold
siguikannt upased angle The mockim abul fwiy-
0,2048, o 6660, 1,0628.
vfww indicated by <thinkc
Figure 5: Ablation study on the camera-controllable generation. We evaluate the effectiveness of thinking
mode (left) and the precise geometric encoding provided by camera map (right).
3.2 EVALUATIONS
GENERATION
Settings. We evaluate our generation performance against the state-of-the-art method Precise- Cam (Bernal-Berdiee et al. 2025). In addition. compare our approach with recent powerful multimodal models, including GPT-4o (OpenAl 2025). Qwen-Image (Wu et al. (025a), and Nano Banana (Google DeepMind 2025), using the same captions as method. The prompt templates shown in Appendix A2.3, To mitigate the data gap for the multimodal models, we convert the camera parameters in captions from radians degrees or express them using professional photographic terms. For quantitative evaluation, we adopt an offline method (Veicht et al. (202)) to estimate pixel-wise camera maps. Using the ground truth maps, we then compute the mean and median errors of the up vector, latitude, and gravity, all measured in degrees. We also incorporate the standard FID metric to assess the overall visual quality of the generated images. Since no benchmark dataset exists for text-to-image generation with precise camera parameters, we construct Puffin-Gen to fill this gap. The dataset consists of 650 caption-camert pairs spanning diverse scenarios and camera configurations. The construction details of Puffin-Gen and Puffin-Und are presented in Appendix A3 and we will release them to support standardized evaluation and facilitate subsequent works.
Comparison Results. We report quantitative and qualitative results in Table 2 and Figure 4 Our method outperforms existing multimodal models by a large margin across all metrics. While these models produce high-quality and aesthetically pleasing images, they fail to ensure spatially consistent layouts under specific camera configurations. PreciseCam (Bernal-Berdun et al., 2025) provides effective control but often generates monotonous stylized outputs (e.g., anime with limited diversity, and struggles with challenging configurations such as significantly tilted poses. In contrast, our method generalizes well across diverse scenarios and camera settings, demonstrating strong practicality for real-world image generation. Additional results and crrameterr-peciffe controls are shown in Appendix A5.2 (Figure All and Figure A12).
3.3 ABLATION STUDIES
Architecture. As discussed Section 2.1, directly fine-tuning the existing VLMM yields a significant performance bottleneck since their vision encoders learn overly condensed high-level features and language models have little prior knowledge of spatial perception. As listed in Table 3 directly finetuning the current VLMs (Bai et al. 2025 Zhu et al. 2025) even underperforms the vision-only network. To this end, we carefully pair an LLM (Qwen& et al. 2024) with the vision encoder (Heinrich) et al. 2025); both of them first aligned and then fine-tuned on the camera understanding dataset. By joinly integrating the geometric perception and context understanding in a staged optimization manner, method (Ours) outperforms the above approaches all evaluation metrics.
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Prompt: Guide to take photo with nigher apolo besthetics.
Suggestion: bose with the deviation of saramedars year +3.43*
Reference Image
Virtual 3D Object Insertions
Photography Guidance*
Inital Views
Gerembed Views
Reconstrction Resuls
Prompe envronment Imagine nddnn your gott.
Imagination Descriptio: A cozy room with wooden fumitaro, W.liddow with curta nG, and 12t
Source Eestrorment
Spatial Imagination
World E Exploration
Figure 6: Applications of the proposed Puffin. Our method can help 3D object insertion into wild image by predicting its camera parameters. Additionally, it can flexibly extend to various cross-view tasks such as the spatial imagination, world exploration, and photographic guidance, by instruction tuning".
Thinking with Camera. To mitigate the modality gap between camera and vision-language, we introduce thinking with camera. For camera understanding, we align spatially grounded visual cues with photographic terms across geometric context, enabling LMMs predict camera parameters through structured spatial reasoning. As shown in Table 3 this design (Ours w/ Thinking) consistently improves performance, especially for pitch and FoV prediction that depend on broader contextual priors, demonstrating the framework's ability to capture hierarchical spatial context beyond localized geometric cues. Thinking with camera also enhances camera-controlllabe generation: given a prompt with scene descriptions and target parameters, the model infers potential spatial cues and uses them as a semantic planning stage to guide synthesis. As illustrated in Figure 5 (1eft), emphasizes visual cues such as ceilings under large tilt-up, yielding more accurate spatial simulation.
Camera Parameters vs. Camera Map. Beyond discrete camera tokens derived from explicit numerical parameters, further introduce continuous representation of camera geometry via pixel-wise camera maps for controllable image generation. We show the effectiveness the precise geometric encoding provided by camera map in Figure (right). (right). Compared to numerical values of the camera parameters, the camera map encodes the local geometric context at each pixel, including orientation and spatial displacement clues, offering precise control over spatial layout and viewpoint. Without the camera map as conditions, generated images may exhibit severe geometric distortions and inverted spatial illusions under challenging camera configurations.
3.4 APPLICATIONS
We illustrate the versatile capabilities of our Puffin in Figure Similar previous methods (Hold- Geoffroy et al. 2018 Jin et al. 2203), Puffin can support virtual 3D object insertion into in-the-wild images by accurately predicting camera parameters. Furthermore, it can flexibly extended to range of cross-view tasks through instruction tuning, such as spatial imagination, world exploration, and photographic guidance. For both the initial and generated views in world exploration, we visualize 3D reconstruction results with VGGT (Wang et al. 2025a), showing proper spatial consistency across viewpoints. Additional results are presented in Appendix A5.3 (Figure A13
A14 A15).
4 CONCLUSION
We introduce Puffin, a unified multimodal model that jointly performs camera-centric understanding and generation across arbitrary viewpoints. These two tasks have been commonly treated as isolated problems and independently explored by the research community. Yet. in essence, they represent two complementary sides: decoding the geometry the world and encoding back into controllable. perceptually consistent visual content. Unlike previous unified models restricted oversimplified

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Published as a conference paper at ICLR 2026
front-view assumptions, Puffin eliminates the modality gap by interpreting the camera language and leverages the notion thinking with camera. We argue that unifying camera-centric understanding shared representation of camera geometry,
and generation anchors perception and synthesis allowing machines to reason about space more holistically and interactively. Such a unified camera- centric model underpins robust spatial intelligence and fosters more versatile applications.
ACKNOWLEDGEMENTS
This study is supported under the RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). is also supported by Singapore MOE AcRF Tier (MOE-T2EP20224-000)
We thank Zhouxia Wang, Zongsheng Yue, Haiwen Diao, Zhaoxi Chen, Zhijie Shen, Xiang Li, Qiqi Gong. Edurne Bernal-Berdun and Koki Maeda for their insightful discussions. We thank Xu Song and Juncheng Zhou for their help with the comparison methods.
REPRODUCIBILITY STATEMENT
We have made every effort to ensure reproducibility. The main paper provides complete description of the framework, and the appendix details the implementation, dataset, training recipe. We have released the code, models, dataset construction pipeline, and benchmark to further advance research multimodal spatial intelligence.
ETHICS STATEMENT
We affirm our adherence to the ICLR Code of Ethics and have conducted this work with integrity and responsibility. Our research does not involve human subjects, sensitive personal data, or applications intended for harmful use. We aim to contribute positively to the community by releasing resources that promote openness, reproducibility, and fair advancement of research.
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APPENDIX
In this document, we provide the following supplementary content: related work, implementation details, dataset construction, training recipe, additional experiments, limitation and future work, and statements.
A1 Related Work
17
AI.I Large Multimodal Models
17
A1.2 Unified Multimodal Models
18
A1.3 Camera Geometry from Vision
18
A2
Implementation Details
18
A2.1 Network Configuration
18
A2.2
Camera Parameters to Photographic Terms
19
A2.3
Prompt Template for Multimodal Tasks
19
A3
Dataset Construction
20
A3.1
Panoramic Data Collection and Preprocessing
20
A3.2 Perspective Image Generation
21
A3.3 Scene and Spatial Reasoning Captions
22
A3.4
Cross- View Dataset
22
A3.5
Prompt Design
23
A4
Training Recipe
23
A5
Additional Experiments
24
A5.1
Camera Understanding
24
A5.2
ContrlaCCn Generation
25
A5.3
Downstream Applications
26
A5.4
Ablation Study
26
A5.5
Analysis
26
A6
Limitation and Future Work
28
A7 Statements
29
A1 RELATED WORK
A1.1 LARGE MULTIMODAL. MODELS
Built upon a visual encoder (Radford et al. 202 Z Zhai et al.
2023 Tschannen et al. 2025 and large
language model (L.M)) (Touvron et al.) 2023 Owen al.
Cai et al. 2024 Liu al. 2024), LMMs (Liu et al. 2023 Chen et al.) 2024 Tong et al. 2024a Bai et al. 2025 Thu et al. 2025) process mixed visual and textual inputs and perform understanding and reasoning via language generation. Fueled by large-scale pre-training of the vision and language models and sophisticated instruction- tuning, LMMs excel at high-level understanding tasks, such as object localization, counting, and optical character recognition. However, these models, optimized for semantic alignment between vision and language, remain limited in capturing image intrinsics (e.g., depth and geometry), which constrains their ability in camera understanding and spatial reasoning. To bridge this gap, it is erucial
2024
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to enrich LMMs with geometry-aware prior knowledge that preserves structural details beyond semantics. Moreover, aligning such geometric cues with linguistic tokens provides a pathway to extend the reasoning capacity of LMMs from abstract semantics to physically grounded spatial understanding.
A1.2 UNIFIED MULTIMODAT. MODELS
As an extension of standard LMMs, unified multimodal models (Team] 2024 Wang et al.
2024 Tang
et al. [0225] Wu et al.] 2025dd Lin al. 20255b Wu al. [024 [ong al. 2024b Pan al. 2025
Lin et al. 2025a Wu et al.] Chen et al. 2025 Wu et al. (005bb] Xie et al. 2024 (025a) jointly
learn visual understanding and generation within a single framework. Two main design philosophies are typically adopted. One line of work formulates visual generation as autoregression over either
Wang et al. 2024, Wu al. 2025b) or continuous (Fan parameters for both understanding and generation. Another Wu eu et al. 2025e Lin et al., 2025a) aligns pre-trained LMMs with diffusion modules, enabling faster convergence and lower training cost. While both types of models advance general image understanding and generation, they are constrained to simplistic camera assumptions (e.g., fixed front-view, predefined FsVs), hindering their practical applicability to realistic and complex environments. To this end, we introduce a camera-centric framework that jointly performs camera understanding and controllable generation.
discrete (Team 2024, Wu et al., 2024
it al. (025) image tokens, sharing LLM
line (Pan et al. 2025; Chen et al. 2025;
A1.3 CAMERA GEOMETRY FROM VISION
Tasks such as camera calibration and pose estimation have long been a central topic in 3D vision (Polle-
feys et al. 1999 Hartley & Zisserman, 2003 Liao et al., 2023 Veicht et al. 2024 Geoffryyy
et al.) 2018 Jin al.) 2023] Zhang et al. [024 Lin et al.] (025cc.. While earlier learning- based
works attempted to directly regress camera parameters from input images Hool-Geeffrry et al.
2018 Workman et al.) 2015 Bogdan et al. 2018 Zhai et al. 2016 Kendall et | recent
advances increasingly explore the use of intermediate representations or geometry fields to bridge the prediction gap. Representative approaches (Lee et al. 2020; 2021; Song al. [024 Janampa & Pattichis 2024 Yin et al.) 2018) leverage geometric structures or semantic features to alleviate the
inherent difficulty of inferring camera parameters from few views. Building on priors of the camera model and the perspective properties of captured images, a growing body of methods proposes to learn dense geometry fields, such as distortion distribution maps (Liao et al./ 2020; 2021), pixel dis- placement fields (Li et al.] [2019] Liao al.] 20255 Xie et al., (0255)) camera rays (Zhang et al., (022), perspective fields (Jin et al.] (0023 Veicht et al. 2024 Tirado Gardo & Civera 2025). or incidence fields (Zhu al. 2023 He et al.] 2025; Deng et al.) (022). However, such representations typically emphasize low-/mid-level patterns, limiting their ability to capture a holistic and coherent spatial
concept. Rather than pursuing better representations, this work explores alternative perspective: interpreting the camera as language.
A2
IMPLEMENTATION DETAILS
A2.1 NETWORK CONFIGURATION
For the architecture of our Puffin, we use the pretrained C-RADIOV3-H (Heinrich et al., 2025). Owen2.55-1.5B-Innstte (Qwen al. 20224,, and SD3-Medium (Esser et al. 2024) to initialize our geometry-aligned vision encoder, LLM, and diffusion model, respectively. Learnable queries with the number of 64 and lightweight connector comprising six transformer layers are exploited to translate the LLM hidden states to conditioning signals for the diffusion model. The resolutions of the image and camera maps are set 512 x 512 for all tasks. For tokenization, the camera parameter tokenizer follows the same procedure as the text tokenizer. Since camera parameters are numerical, we first serialize them into discrete tokens, which are naturally handled by the standard text tokenizer without requiring any extra module. Introducing an additional tokenizer (or separate text encoder) would substantially increase the alignment burden across modules and modalities in a unified multimodal model. For this reason, we keep the vanilla decoder-only LLM backbone and its tokenizer to process both language and camera parameters. For the camera map, we adopt Perspective Field (Jin et al.) 2023). We first normalize its values to the range 1, 1). and then reuse the image tokenizer (i.e., the
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Table Al: Camera parameter-to-teni mapping. To align camera parameters (roll, pitch, and FoV) with the prior knowledge space LMMs, their numerical ranges are mapped to professional photographic terms. Combining different terms linguistically allows us to jointly describe the full camera geometry of an image.
Roll
Term (1)
Large counterclockwsse
Small counterclockwise
Near level shot
Small clockwise Dutch angle
Large clockwise angle
angle
Dutch angle
Example
Parameter (p)
1-15%--00 20
[-20% -5%)
(6"200)
(20)
Pitch
Term (1)
Large tilt-down
Small tilt-down
Near straight-on shot
Small tilt-up
Large tilt-up
Example
Parameter
[-45%, -
[-20% -
[-5%,69]
(69,20%
(20%.459) 445)
FoV
Term (1)
Close-up
Medium shot
Wide-angle
Ultra wide-angle
Example
Parameter (p)
(20)
[35"65"") 65")
665" (65".90")
990 105
VAE encoder), as the camera map also has three channels. Pretraining specialized tokenizer for camera maps is left as future work.
A2.2 CAMERA PARAMETERS TO PHOTOGRAPIIC TERMS
To bridge the gap between the detailed numerical values of camera parameters and the highly abstracted understanding capability learned by LMMs, propose using professional photographic terms as intermediate supervision for our framework. Specifically, we quantize the range of each camera parameter and map them to the following photographic terms: (i) Roll: large counterclockwise Dutch angle, small counterclockwise Dutch angle, near level shot, small clockwise Dutch angle, large clockwise Dutch angle; (ii) Pitch: large tilt-down, small tilt-down,
straight-on shot, small tilt-up, large tilt-up: (iii) FoV: close-up, medium shot, wide-angle, ultra wide-angle. The detailed mapping relationship between the camera parameters and photographic terms is listed in Table Al
A2.3 PROMPT TEMPLATE FOR MULTIMODAL TASKS
For text-to-image controllable generation, we use the following prompt template format user instructions: User: Generate an image: <caption>>y Assistant:", The <caption> includes both the image description and the numerical camera parameters (roll, pitch, FoV, all in radians). For camera understanding, we employ the following prompt template to format the basic user instruction: User: <image><<uestions The <questions on> can be set as "Describe the image in detail. Then reason its spatial distribution and estimate its camera parameters (roll, pitch, and field-of-view) For cross-view camera-controllabbe generation, the prompt template is formatted as: "User: Generate a target image given an initial Assistant:". Here, <image> denotes the initial view token from the image tokenizer, while <caption> represents the target image description along with the target camera parameters (roll, pitch, yaw, and FoV, all in radians). During cross-view instruction tuning, we randomly set the <caption: to null with a probability of 0.5, thereby enabling both text-free and text-conditioned image-to-image generation. When applying the spatial reasoning paradigm, we switch to a new <question> for camera under- standing: "Reason the spatial distributio on of this image in a thinking mode, and then estimate its camera parameters (roll, pitch, and field-of-vil -ovivi ". For generation, we first enrich the vanilla prompt using our model with the template: "User:
Assistant:".
view: <image><caption>>
\n Assistant:" Here, <captions refers to the vanilla image description, and <question> is "Given a scene description and corresponding camera parameters, merge them into a coherent prompt and generate an accurate visualization that highlights visual cues for spatial reasoning". For other instruction tuning tasks, <question> is set "Given
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Fov Varkants
Plach Vartarts
Pulfie-4
Sardard View
Figure A1: Overview of the proposed Puffin-MM dataset. consists million risionllaguuage-camen triplets under various scenarios and camera configurations. We mark the sample images with different colors, each denoting a different variant of the camera configurations.
the initial view and the camera parameters of the target view with the deviation yaw angle, how would you describe the target image to build replica of the scene?" for spatial imagination, and "Estimate the camera parameters (roll, pitch, and field-of-view) of this image. And then predict the deviation camera yaw angle and pitch angle of the target view with high photographic aesthetics." for photographic guidance.
A3 DATASET CONSTRUCTION
The overview of our Puffin-4M is shown in Figure AI The construction of this curated dataset consists of four stages: panoramic data collection and preprocessing perspective image generation, scene and spatial reasoning captioning, and extensions for cross-view scenarios. Following previous works (Veicht et al. 2024 Jin et al.) 2023] BBrnnllBBerun et al. 2025; Hold-Geoffroy al., 2018). we render the perspective images with various camera intrinsic and extrinsic parameters from 360 panoramic images using standard camera model. The pipeline of the dataset construction illustrated in Figure A2 and the dataset comparison with previous works is listed in Table A2 Puffin Und and Puffin-Gen are constructed by exactly the same pipeline as Puffin-4M: the only difference lies in the source images and the task-specific splits. More details described follows.
A3.1
PANORAMIC DATA COLLECTION AND PREPROCESSING
We begin by collecting panoramic images from publicly available datasets (Bertel 2020 Choi
et al.
2023
Cao et al., 2025; Huang et al.) 22244 Xu et al.) 2022 Zhang et al. 2020 Bernal Berdun
al.
2023
Cheng et al.) 2018 Boldue et al.] 2023] Chang al. 2018 Veicht al. 2024 Armeni
et al.
2017
as well as from online platforms (Flij [tr) Wik HDR Pol] Amb) Ble) You). In addition,
we acquire a large volume outdoor panoramic data from Google Street View (Goo). spanning 12
cities across Asia, Europe, and North America. In total, our curated dataset comprises approximately 200K high-quality panoramic images with substantial diversity. A significant portion of these images reaches 4K resolution or higher, up 10K. However, due variations in 360 camera calibration and acquisition stability, some panoramas exhibit geometric distortions and misalignment. To mitigate this, we apply geometric correction techniques based line segmentation and vanishing point
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Single-View Dataset
Perspective Images
YLM
Panoramic mege
Scarm Castors
Retned Captons
Cemera Patomelerss
TRide
Stralhhiin
Arobe
Augo
Orgiees
Maps
Virtual Camara
Bevey
Sprial Reasoning
Photographic Terms
C Cross-View Dataset
Farameters
Perspective Images
PIONEC BES
Tapres
Scone Capdorns (Tangetvlen)
Perspeciive Images
Virtual
Photographic Awchediss
Figure A2: Pipeline of the dataset construction. P2T denotes the mapping from the numerical camera parameters to the professional photographic terms. For clarity, omit the orientations "clockwise" and "counterclockwise"" of the Dutch angle in photographic terms.
Table A2: Dataset Comparisons. The datasets proposed in previous individual models tailored for camera understanding (Lee et al. 2021 Bogdan al. 2018 Veicht et al.) 2024 Jin al. 2023 Hold Geoffrey al. 2018) or camera-controllabbe image generation (Bernal- -ernnn et al. (0255) vs. our Puffin-MM for the camera- centric unified multimodal model. In addition to its larger scale, our dataset also offers advantages in spatial reasoning captions, and cross-view image pairs. For the camera parameters, we denote the intrinsic parameters: focal length (f), radial distortion coefficient (O): and the extrinsic parameters: roll (Ø), pitch (0), yaw (v)).
Task Type
Extrinsies
Camera
Text
Reasoming
Single View
Cress- View
GooCalib
Veccit et 2024
Understanding Under atading
[f.4]
[d.0]
37K
CTRI
[5.9]
45K
Decpeslin
Understading
Understandngg
(o.0]
Pereeptual
Inderstanding
[0.0]
PreciseCunf [pzrmu Decumn
Generation
16.33
[0.01
57K
Pullin-4M (Ours)
Unified
10.0.0]
4M
estimation (Jiang et al. 2022 Zou et al. 2018). aligning the panoramas with the gravity direction and improving structural consistency.
A3.2 PERSPECTIVE IMAGE GENERATION
We adopt the pinhole camera model with varying intrinsic parameters (vertical FoV) and extrinsic parameters (roll and pitch) to synthesize perspective images, following the protocol established in recent state-of-the-art camera calibration works (Veicht et al., 2024 Jin al. 2023). For each panoramic image, we generate multiple perspective crops by uniformly sampling roll, pitch, and vertical FoV within the ranges [-450, 45%,, 1-450 45%), and [20", 105%]. respectively. The number of crops is adaptively determined based on the resolution of the original panorama. While our current setup assumes ideal pinhole model, incorporating radial distortion effects via additional distortion parameter k is left as future work. After generating the perspective images, we further convert the corresponding camera parameters into pixel-wise Perspective Field (Jin et al.) (023) as camera map, where each pixel is annotated with its up-vector ux and latitude angle 4x to enable fine-grat -raingd spatial encoding:
P(X - cg) -
R |
lim
8x aresin
[P cg) P(X)
where P(X) = x denotes the mapping of a 3D point X to an image pixel x, and each pixel x corresponds to light ray R originating from X.
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448 [Syaten hole Instroction] Given that the caporing camera informatio image known horiznnaal or enietttion: large cockwise Dutch angle: oriontation: Reasening feddef-dee
exactly integiwea endosed within rish speid cusa from boll local sumanics and
single
explonation withcut redundancy. Your oubbut
<illnkk and <iiiis lugs The soplenation alould reflect visualy grounded reastring global scene layaut Struttre the response into three conciso sentences, each contibbting to the overal inference without enpy labeling vkduu camera perametars or discr bing the genent acera. s. Do not includ any axt information or format deviations
<think> imaga
by perpacttivn
groune structures maintain consissnn vertical lines
pathway the frame's The orientation miremel liI the dieccton. The wileeangle Bekt-afooww appaoeet from the ecpenssvve view the park, browd
wigremett wilh the finenaaa wericoll range of elements includin the pathway, benches, trees, and distant buildings, which are visible withi a single frome, indicating broad spatial coverage. <thinte
(a) The prompt to generate the reasoning caption for thinking with camera.
#*ll [Syatus Role Instruction]
You are expert photography (udgo. For each input image, where only camera you and pichh vary. ovaluate is aosthetic quality using the folimiing 4 crtaris (lowe 10 pointe
1. Viewpoint Creattely (0-2 poinis): la the perspaction (ywolplch)) axpr express va or unique? Dows offer a refreaking view of the subject scene?
Subjeci Erephasis (0-2 pointa): How will does posa Highiihtt isclate main subject? frarning intenioonal and focused?
golden piacement and olement alignment coharent? Spaital Hemonnzation (0-4 prints] Down the somne whhibit hemprired spatial distrbition? Are foregrundd malgruund and baskground aranged to conveyy depth or balance? Does the space foel cono and deliberat??
main subject complete and
point artullyy Sttef
Compositioal
visualy
The depicts small train-ike velicle in the foreground, and few people scatered across the soone. The sky is partly dloudy adding dynamic oloment to the composition Bolow the mnlioiion based on the aperited crtarie:
with archtnctural etements, inclucting prominent church with spro. modemn buikiing and red-ont buildig There
Aka Viewpoint Creativity (Score: 123)=
affers berspecthie appears a standard eye level view which common for capturing urban scenes. There particularty unique expressive yow or pitch angle that
mmaghhtonwond funstional, focusing
scene rather experienting with perspective.
paas "2. Subject Emphasis (Score: 122).
The main sutject appears be the chirch with the tall spim, which is eigntticnt archttcturi teature in the backgrundd However, the subject not the sole focus; the imaga wiso nccudes the train, people, and other huidings. which abone the vssiel attenton.. The framinn is somewhat balanced but not fightly fooused on single subject. The church positoned near the left side, adhoring somowhat to the rule of thirds, but it not the only paint of intewsd
peste Compositioal Balance (Score: 2/21.
horbonn
eligned axtwrently The burllingg the trai the red red-onck
of right. The distttution elements
buling
balanced, intantional and arganaad
The mage
church the the
enne "4, Spattal Harmonization (Score: 4M4--
Thee image effccively convays depth through the ar anangement of the foroground, midground, and background. The cobbresione pavement in the foroground, the zain and huilding backgrond create a layoned compostion people in matgmandd harmonoous. with each element contributng the overal balance, church spire draws eye upward, adding vertical Interest, the The spatial disrisution
buildings
"Total Bcors: ar10
(b) The prompt to generate the photographic aesthetic score for the photographic guidance task. Figure A3: Examples of the designed prompts for captioning our dataset.
A3.3 SCENE AND SPATIAL REASONING CAPTIONS
Captions are essential for multimodal understanding and generation. To construct high-quality descrip- tions, we first utilize Qwen2.5-VI. -L--5---V-7---sttruue (Bai et al. 2025) to generate semantically rich captions for each perspective image. These are subsequently distilled using Qwen2.5-7BB -B--struuttt (Qwen et al., 2024) into shorter, vivid, and visually expressive sentences.
To bridge the modality gap between camera geometry and vision-language representations, we introduce the notion of thinking with camera that explicitly guides multimodal tasks using spatially grounded visual cues and professional photographic terms. For captioning such a spatial reasoning process, we propose a two-step strategy: first, we map the numerical camera parameters to their corresponding semantie photographic terms (see Table AT); then, for each camera parameter, use its corresponding photographic terms to retrieve and prompt out relevant visual concepts from the LMM's prior knowledge. For the trade-off between accuracy and efficacy, we employ Qwen2.5-VL- 32B-Instruct to generate the final spatial reasoning captions.
A3.4
DATASET
In addition to the single-view dataset construction described above, we further enrich our dataset with cross-view data involving various tasks. Specifically, during cross-view perspective image generation, we extend the camera's 's degrees of freedom by incorporating the yaw angle in addition to roll and pitch, sampling it uniformly from [0 360%). The initial view is initialized with a standard camera pose (roll pitch yaw = 09), and the target view is rendered using a random camera pose sampled within the aforementioned ranges. The target view in each cross-view view pair is then captioned using Qwen2.5- Vint-tstttt
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Table A3: Training recipe of Puftin. For the data sampling ratio, mark the data involving the spatial reasoning and instruction tuning in light blue and light red respectively. For clarity, abbreviate the generation and understanding as Gen. and Und.
Stage
Stage II
Stage III
Stage IV
Hyperparameters
Learning rate
10
x
LR Schedulerr
Cosine
Weight Decay
0.05
Betas
0.95)
Optimizer
Adamm
Batch Size
1024
256
Training Steps
10K
30K
60K
20K
Warm-up Steps
900
1.8K
600
LLM
Frozen
Trainale
Trainable
Trainabee
Diffusion Model
Frozen Frozen
Trainable
Trainable Trainable
Trainabee
Vision Encoder
Trainable
Frozen
Data Sampling Ratio
Text Canera- (Gen.)
0.5
0.0
0.0
Image- Image->Text.ee /Text-Cammrrtaa (Und.)
0.5
0.0
0.0
Text-+Txx
0.33
0.0
Tex--Caeer-+Hmmag (Gen.)
0.0
0.0
0.33
0.0
Image->Text- Text-Carraa (Undd)
Image-Camera-- Text (Cross-view Und.)
0.0
0.0
0.0
0.47
Image-(Text))
-Image (Cross-veew Gen.)
0.0
0.47
Image Camera (Phatography Und.)
0.0
0.06
For the photographic guidance task, we first consulted photography experts and enthusiasts identify four key aspects of photographic aesthetics: viewpoint creativity, subject emphasis, compositional balance, and spatial harmonization. These are then formulated into four criteria that serve as aesthetic rating prompts for LMMs. We focus pitch and yaw as the key controllable camera parameters ? An initial view is generated with random pitch in -00 20%), and N neighboring views are sampled by perturbing pitch and yaw within the same range. All rendered views are evaluated by Qwen2.5- VL- 32B-Instruct using the aesthetic prompts, and scores are assigned through voting. The pitch and yaw offsets between the initial view and the highest-scoring view are taken as labels for the photographic guidance task.
A3.5 PROMPT DESIGN
For the scene caption of each image, the prompt is formatted as: "Describe this image in 3-4 sentences". Then, we refine the caption into a more compact description using: "Here is a detailed image descriptions shorter, vivid, and visually rich sentence (one or two sentences) that captures only the most essential elements and atmosphere of the scene. Ensure the description is concise, clear, and optimi zed for use with text-to-image generation model". For captioning the spatial reasoning and photographic aesthetics of each image, we show the corresponding prompts and example results Figure A3
<captions Rewrite it into much
A4 TRAINING RECIPE
The whole training process takes around days with 64 NVIDIA A100 (80 GB) GPUs. In reference, we use greedy search for text generation in camera understanding and set the CFG weight as 4.5 for the controa-ont image generation. We conduct a multi-stage training strategy, where the vision encoder, LLM, and the diffusion model are aligned in the first stage. Then, in the supervised fine- tuning (SFT) stage, the models are jointly optimized using both base and thinking datasets. Finally, an instruction-tuning stage is applied, involving various cross-view generation and understanding tasks. We elaborate each training stage as follows.
Stage I-Alignment. In this stage, we align the vision encoder with the LLM by training only the MLP projector for the understanding task, where the framework learns to predict
2 Both professional and amateur users generally prefer near level-shot photography, as humans are highly sensitive to horizontal perturbations (Howard & Templeton 1966 Dyde et al. 2006). Thus, we fix roll at (1 for this task.
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Table A4: Additional evaluation results camera understanding.
Roll [degrees]
Pitch [degrecs]
FoV [degrees]
Approach
error
A 1/5/10"1
eeror 4
AUC 1/5/10"1
crror
AUC 1/5/10"1
DeepCalih Lopez et al
2019
1.59
31.8
63.9
79.2
2.58
21.6
46.9
65.7
6.67
8.1
20.6
37.6
Perceptual
2018
2.08
26.8
53.8
70.7
3.17
21.5
41.8
57.8
13.84
7.7
16.1
Stanford
CTRL-C [Lee al
204
3.04
23.2
43.0
56.9
3.43
18.3
38.5
53.8
8.50
7.7
18.2
31.5
MSCC Song.etal
3.43
13.5
36.8
57.3
2.64
45.0
5.81
23.8
41.6
ParamNer Dun c1
2014
1.14
44.6
73.9
84.8
1.94
29.2
56.7
73.1
9.01
5.8
14.3
27.8
SVA Lochman al 2022]
21.7
24.6
25.8
15.4
19.9
22.4
6.2
11.5
15.2
UVP PPaurall 20233
0.52
65.3
74.6
79.1
0.95
51.2
63.0
69.2
3.65
22.2
39.5
51.3
GenCalib Veicht ot al
2024
0.40
83.1
91.8
94.8
0.93
52.3
74.8
84.6
3.21
17.4
40.0
59.4
Puffin (Oirs)
0.26
96.6
99.4
93.6
96.7
2.30
23.4
71.4
both text descriptions and camera parameters from the input image. For generation, the framework takes text descriptions, camera parameters, and the camera map as inputs, and learns to synthesize the target image with the corresponding description and configuration. Specifically, we train learnable queries and a connector to bridge the LLM and the diffusion transformer, where the connector maps LLM hidden states into conditioning signals for the diffusion model. A cross-entropy loss and diffusion loss supervise the understanding and generation, respectively, while parameters of the vision encoder, L.M,, and diffusion model remain frozen.
Stage H-SFT. After aligning different modalities, we unfreeze all modules except the VAE and fine-tune the entire framework, using the same inputs and outputs as Stage I. To stabilize training, we apply gradient scaling of 0.1 the vision encoder.
Stage M-SFT w/ Thinking. To further bridge the modality gap between the camera and langunn,anguage, we introduce thinking with camera this stage. The implementation is the same as Stage-II, except that the training data contains spatial reasoning captions (the details of obtaining such captions are provided in Section A3). Beyond generation and understanding, this stage also learns the textual reasoning task, which enriches the vanilla captions with spatially grounded visual cues and translates specific camera parameter values into professional photographic terms.
Stage IV- IV-Instruction Tuning. Finally, we improve our model's ability to adapt to diverse spatial configurations. In particular, three types of cross-view data are trained simultaneously, including the spatial imagination, world exploration, and photographic guidance. The KV cache mechanism is utilized in cross-view generation. The vision encoder is frozen while other modules are trainable.
We release three model variants: Puffin-Base, Puffin-Thinking, and Puffin-Instruct, to accommodate different application needs. Puffin-Bass provides a foundation model for unified camera under- standing and camera-controllabe image generation; Puffin-Thinkee enhances spatial reasoning and generation; and Puffin- Instruct is optimized by instruction tuning, supporting cross-view tasks and complex multimodal interactions.
A5
ADDITIONAL EXPERIMENTS
A5.1 CAMERA UNDERSTANDING
Results. Note that our training dataset consists of images rendered from the source panoramas in Stanford2D3D (Armeni et al., 2017). Although the sampled perspective images and camera parameters differ from those the test (Armeni et al. 2017). we exclude these results from the main evaluation to ensure rigor and report them in Table A4 only for reference.
We show more visualization results on the proposed thinking with camera for camera understand- ing in Figure A8 Qualitative evaluations of the camera understanding methods with horizon line visualization are illustrated in Figure A9 Compared to prior approaches, our Puffin demonstrates strong performance not only in common scenarios such as architectural and indoor scenes, but also in challenging cases characterized by limited geometric features or significantly tilted camera poses. These results highlight the robustness of the proposed method. We visualize additional camera understanding results (with camera maps converted from the predicted camera parameters) on diverse inputs, including AIGC images (OpenAl 2025) and real-world photographs, in Figure A10
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GPT-40
Cwan-lmage
Nano
ProciseCam
Rell
9
Plah S7
FoV
Figure A4: The predicted vs. ground truth camera parameters across all generated samples. Compared with previous methods, our generated results well align with the distribution of the ground truth camera parameters.
From the evaluation of existing methods (Table 1), we observe that estimating pitch and FoV is considerably more challenging than estimating roll. This difference arises from the nature of the underlying visual cues. Roll estimation is supported by mid-level geometric representations, such as edges and vanishing lines, which are directly embedded in the image structure and thus relatively straightforward to learn. In contrast, accurate estimation of pitch and FoV requires more extensive contextual priors. Unlike previous vision-based approaches, our method explicitly models the relationship between physical camera parameters and spatial context using large multimodal model. This integration allows the model to capture spatially contextual knowledge that cannot be sufficiently represented through visual features alone.
Discussion.
A5.2 LAMERA-CONTROLLABLEE GENERATION
Results. Our camera--onttoollble generation results with various camera configurations shown in Figure AIT, and the cox-tt--imaee generation (single-view) results with specific controls for each camera parameter presented in Figure A12.
Discussion. We further conduct an in-depth analysis to understand why existing image generation models fail to achieve accurate spatial simulation. Specifically, decouple the spatial distributions of the generated images with respect to three camera parameters: roll, pitch, and FoV. As illustrated in Figure A4 we visualize scatter plots of the predicted vs. ground truth camera parameters (with the reference line æ) across all generated samples. For fairness, the predicted camera parameters are obtained using the state-of-the-art vision-based camera calibration method (Veicht et al. 2024).
Interestingly, we observe a reversed role of camera parameters in controllable generation compared with camera understanding. Specifically, images generated by previous methods (OpenAl 2025 Wu
et al. 2025a Google DeepMind] 2025 Biernal-Berdin et al. (2025) exhibit poor simulation accuracy on roll compared to pitch, where the predicted roll values fail to align with the target ground truth. In contrast, roll estimation in camera understanding is generally easier than pitch, due to its explicit link with geometric structures.
We attribute this discrepancy to two main factors: (i) Most existing image generation models are trained on datasets curated for high visual aesthetics. Both professional and amateur photographers tend to prefer near-level shots, as humans are sensitive to horizontal perturbations (He et al. 2013). Consequently, variations in roll often conflict with aesthetic preferences, leading to a skewed dataset distribution with far fewer roll variants compared to pitch Fo V. (ii) Roll directly alters the perceived gravity direction in an image, thereby reformulating the common sense of spatial layout. For instance, strong Dutch angle can make the sea surface appear above the horizon line, creating inverted
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Table A5: Model size comparisons: the specialized understanding and generation models (GeoCalib (Veicht 2024) and PreciseCam "Bernal-Berdur al. 2025)), ). and the proposed unified camera camera-centric model.
Model
Parameters
GFL.Pss
GeoCalib (Veicht et al.
2024
Specialized Model (Unilessaanding)
28.9M
3.47
10
PreciseCam Bemal Herdun
2025
Specialized Model (Generation)
10a
Puffin-MM (Ours)
Unified Model
4,48
2.92
10
spatial illusion. Such cases are inherently more difficult to simulate, whereas pitch and FoV changes typically only affect the viewing scope without fundamentally disrupting the physical law.
A5.3 DOWNSTREAM APPLICATIONS
We visualize more downstream application results by instruction tuning here. Specifically, image- to-image generation (cross-view) results with varying yaw angles are shown Figure A13 World exploration results are provided in Figure A14 Examples of the spatial imagination and photographic guidance are shown in Figure A15
A5.4 ABLATION STUDY
We provide additional results and analyses of the ablation study in this part, especially for the mutual effect between camera-centric understanding and generation.
Single Task vs. Unified Training. In addition to performing multimodal tasks within a unified framework, we aim exploit the mutual benefits between understanding and generation through joint training. Unlike previous works (Pan et al. 2025; Wu et al., (025c)), we jointly optimize both the LLM and the diffusion model across un- derstanding and generation tasks. This strategy avoids the representational bottleneck imposed by frozen modules and fosters bidirectional syn- ergy between the two tasks. As illustrated in Fig- ure A5 a)(c) training the camera understanding component in isolation underperforms compared the unified framework, the generation process contributes auxiliary diffusion loss at the low-level appearance, which implicitly enhances detailed ge- ometric perception. While the performance gain for generation is less pronounced than for under- standing in Figure (5))) notable improvements emerge in challenging scenarios such FoV simulation, which requires prior knowledge regarding precise and holistic spatial understanding within image.
Singre Unifeed
Siill Unified
1.0
85
0.5
80
Pish
(a) Und. Results (Median 1)
(c) Results (AUC@100 11
70
60
50
40
Rai
Pich
Fal
Roll
Patch Fay
(b) Gen. Results (Median Error 4
(d) Gen. Resuits (AUC@1O 1)
Figure A5: Ablation study the mutual effect hetween camera understanding (a)(c) and camera- controllable generation (b)(d) supervision.
For other general-purpose purpose unified models, the understanding tasks mainly target high-level concepts such recognition and semantic comprehension. As result, the domain gaps across multimodal tasks are more pronounced in these models, likely requiring more delicate architectural designs to harmonize representations across different modalities.
A5.5 ANALYSIS
Model Size. We show the comparison results on the total parameter count and FLOPs with previous understanding and generation models Table A5, such as GeoCalib Veicht et al. (0224) and PreciseCam Jin et al. (2023). While Puffin is larger than previous specialized models, it replaces separate understanding and generation networks with a single unified model that handles both tasks within one framework. This design not only simplifies deployment, but also allows us to fully exploit nigh-capacity backbone when training on large-scale multimodal datasets. In terms of overall parameter count and FLOPs, Puffin remains substantially more affordable than recent general-purpose unified multimodal models such as Bagel (14B) Deng et al. (2025) and -mage (20B) Wu et al. (2025a).
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Table A6: Additional evaluation results on camera understanding by re- re-training the comparison method Ve icht et al. 2024) on our constructed Puffin-4M dataser*.
Roll [degrees]
Pitch [degrees]
FoV [degrees]
Approach
emror +
AUC 1/5/100 1
emarr 4
AUCD 1/5/1001 1
emur +
AUCp 1/5/10 1
GeoCalih*
Veicht et al. 2024
1.12
46.0
69.3
80.1
2.54
24.5
47.5
65.4
5.47
10.8
25.5
43.8
GooCalib Veicht et al 2024
0.92
53.6
73.9
82.6
2.18
28.9
52.5
69.6
5.04
12.4
28.0
45,8
Puftin (Ours)
0.41
78,3
91.0
95.2
0.74
60.2
81.2
90.0
1.21
42.4
70,5
84.3
Error Analysis. In addition to the metrics (error and AUC) used in the main experiments, we also add the error bars for the representative baselines and method in Figure a) provide clearer and more comprehensive comparison. The results show that Puffin exhibits better robustness across the entire data distribution. The improvement is consistent across all intrinsicsfextrinsics components and remains stable even under challenging camera configurations.
Model vs. Data. We conduct experiments where the comparison method (Veicht al. 2024) is
re-trained on the same dataset (Puffin-4M) as ours, strictly following its original training recipe. Interestingly, we find that the re-trained GeoCalib (Veicht al. (024) on our 4M dataset slightly underperforms the original GeoCalib model trained on its 40K dataset. The detailed evaluation results on the Puffin-Und test set are reported in Table A6
By carefully analyzing these results, we offer two explanations for this phenomenon: (i) Model capacity vs. data scale. GeoCalib (Veicht et al.] 2024) is relatively lightweight CNN-like architecture with around 29M parameters. When trained on a significantly larger 4M-scale dataset with broad scene and distribution coverage, tends to underfit: its limited capacity cannot fully model the entire distribution, so it only fits some sub-distributions well while inevitably neglecting others. (ii) Consistent observations from previous experiments. The GeoCalib authors report a similar trend in their ablation: when training the network on 5x larger dataset, the camera understanding performance slightly degrades on most benchmarks, and no clear improvement is observed; they also show that more advanced architectures can further boost performance. These observations are fully consistent with our findings.
Rel
Richh
Ed)
Gec
Geciabo
(A) Error bar of the camera understanding ovaltuation (Loft: MogaDepth, Right: Puffin-Und)
AUC Rell gs
AUC Pitch gs
AUC FoW g5
631
640
3.5
221
[b) Puffin's s camera understanding perforaance on Puffin-Und ve. the scales the training data
Figure A6: More data analysis of the experiments. (a) We show the error bar of the camera understanding evaluation on GeoCalib (Veicht et al. (2044) and Puffin. (b) We show how the performance of Puffin scales with data size across different camera parameters.
In contrast, Puffin is built on a high-capacity LLM backbone, which, like other large multimodal models (e.g., LLaVA, Qwen-VI., VL.), requires sufficiently large and diverse training data
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Given an infial view, imagine the scane extanding to your nigt.
light outdoor mural with intricate floral designs and lext, against building wall: yellow chair with unique pettern sits in the foreground, while a red struchura and motal railing add to the somee' ambience in a public park-lke seting. The carrera paramelers (roll, pitch, and feekd-of- view) arc:
Generate the
streetlamp
SAMPO Soan
User
Understanding cogy
Pulfin
white
window
sheer curbsins, Huminssed by warm Tol Piro
Puffin
Generation Results
Generate the target image based on the imagination results.
Reason the spatial distribution and estimate its careera perameters by thinking with camera..
User
Understanding Results: <think The image exhibit a large
Putfin
Putin countardacckwis Dutch angle. as evidenced by the perspoctive streellamp, of frame. The vertical oniontation the wall and the chair maintain consisten frontal view. suggesting the camera is posiiioeed directly in front of the scene. The fleld- of-iew is a medium shot, capturnng portion of the street, the wall with the floral tapestry. and the chair, providing a halanced view that includes both the fereground and midground elements. cihiike--anssere 4000. -0,0008 0.7388cianswer
norizontal
Generation Results
Multi-tm interleaved Und. to Gen.
Multi-tum interleaved Gen. to Und.
Figure A7: Multi-turn interleaved capability of Puffin.
avoid overfitting and learn accurate joint distribution over images, camera geometry, and language. In this sense, Puffin-4M is not merely a "bonus", but rather a necessary data regime for such a unified multimodal model that jointly supports both understanding and generation.
Furthermore, we also conduct additional experiments by re-training our model on Puffin-4M of differ- scales (Stage-II SFT). As shown in Figure A6 (b), we observe clear and consistent improvements as the data scale increases. Overall, these results suggest that the dataset and the model contribute jointly and should not be viewed isolation.
Beyond the above scaling results in Figure Figure/A6(bb) we find an interesting difference in trends across camera parameters. For roll, the model learns quickly even with relatively limited data, since mainly relies on low- to mid-level geometric cues that are easy to capture (e.g., strongly slanted lines indicating a large roll). By contrast, estimating pitch and FoV requires more holistic and high- level spatial understanding, which cannot be sufficiently captured by local visual patterns alone and therefore benefits more from larger-scale data to form robust spatial reasoning concepts. This observation is consistent with discussion in Section A.51 Based on these trends, we believe that further scaling the dataset would bring additional gains in camera geometry understanding, especially for pitch and FoV.
Multi-turn Interlcaved Capability. Exploring the multi-round conversational capability of a unified multimodal model is meaningful. To this end, conduct experiments on multi-turn interleaved conversations (generation - understanding and understanding - generation). As shown in Figure A7 Puffin can carry out coherent interleaved dialogues conditioned on previous turns. Specifically, produces consistent cross-view generation results based on its previous reasoning, and accurate camera understanding based on own generated images. This demonstrates that Puffin not only supports both capabilities within single framework but can also use them in an interactive way over multiple conversational rounds, without any task-specific switching or separate models.
A6 LIMITATION AND FUTURE WORK
Because our training dataset constructed at fixed resolution of 512 x 512, Puffin's image generation is currently restricted to single scale. For camera understanding, we applied central cropping followed by resizing (Section]3 3.1), an operation that may discard semantically valid content and degrade performance, particularly when the aspect ratio deviates significantly from unity. While these limitations are orthogonal to our main focus, they could be addressed future work by building multi-scale training datasets. Beyond data design, evaluation of relied on an offline rision-bbsedd calibration method (Veicht et al. 2024). Although this approach reflects the best available practice, the calibration errors it reports can be ambiguous, especially for
generation
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generated images exhibiting only subtle spatial differences. Accurately evaluating spatial simulation thus remains am open challenge and is crucial for advancing camera-controllable generation. We plan to address this by incorporating stronger camera understanding models as evaluators and by designing benchmarks that more precisely capture geometric consistency. In addition, we aim to further enhance Puffin's cross-view capability and extend it to camera-centrie video generation and understanding, paving the way for broader applications in dynamic and immersive environments.
Another interesting future direction is exploring camera-specific tokenizers, which could enable Puffin to model a richer and more nuanced camera control space beyond roll/pitch/FoV. Recent work such as Stable Cinemetrics (Chatterjee et al. 2025) provides a structured, industry-grone grounded laxonomy of cinematic controls. including dedicated camera hierarchy with fine-graines nodes, and shows that even strong generative models still struggle to follow these nuanced camera instructions. This taxonomy and its evaluation protocol suggest principled way to expand our photographic term set and to design more specialized camera tokens/bins that systematically cover lens types, shot grammar, and motion styles. Building on this insight, plan to incorporate taxonomy- sxuided camera tokenization and supervision the next iteration Puffin, together with an extended Puffin-4M covering broader einematic camera attributes, and release the resulting dataset and models to facilitate research on fine-grat aen-igrined multimodal spatial intelligence.
A7 STATEMENTS
Use of Large Language Models. We follow ICLR's policy and use LLMs solely as general-purpose assistive tools. In our work, they were employed to polish the writing of certain paragraphs. They were not used for research ideation, experimental design, or the development of core technical contributions.
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<thinle The image exhibits large counterclockwise Dutch angle, as evidenced by the diagonal orientation the road and the fields, which are not aligned with the image's edges. The significant til-down perspective is apparent from the elevated view of the house and the surrounding landscape, providing clear overhead look the structures and fields. The close-up field-of-view is indicated by the detailed visibiiity of the house's roof and the individual trees, suggesting a focused and narrow scape that emphasizes specific elements within the scene.
<tthnnk<<answer>-00 0.400, -0.6250, 0.4800<ianswer>
<think> The image exhibits large counterclockwise Dutch angle, as evidenced by the filted horizon line and the diagonal alignment of the rows in the field, which suggests a significant rotation of the camera. The vertical orientation shows small tilt-down, indicated by the perspective that places the foreground sunflowers closer the viewer while maintaining a clear view of the background. suggesting a slight downward angle without a dramatic shift. The medium shot field-of-view is apparent from the balanced composition that captures both the detailed foreground of sunflowers and the expansive background of the field, <thinkt-answerr-00.e 0.6400 0.2369, 0.61 1.8134//nsswr>>
The image exhibits significant counterclockwise rotation, evidenced by the tilted alignment of the bookshelf and the wall-mounted objects, which appear to lean towards the left side of the frame. The large till-up apparent from the perspective, where the ceiling and upper wall prominently visible. while the floor and lower elements are minimally shown. suggesting the camera angled upwards. The wide-angle field-of-view is indicated by the broad capture the room's interior, including multiple elements like the bookshell, wall art, and gultar, all within a single frame, which emphasizes the expansive nature the shot. othinle-<answen 0,5814, 1. 1,5348</answer>
<thinke
0,6000,
The image exhibits a significant counterclockwise Dutch angle, as evidenced by the tilted alignment of the buildings and the snowbank, which appear to lean towards the left side the frame. The large tilt-down orientation is apparent from the perspective. where the ground and snowbank dominate the foreground, while the buildings are positioned higher in the frame, suggesting a downward-angled view. The ultra-wide-angle field-of-view is indicated by the expansive capture of the snowbank and the surrounding environment, with noticeable distortion at edges, particularly the curvature of the snowbank and the buildings, which emphasizes the broad scope of the scene. <thinkk><nsswrr-005B88 -0.4957, 1.7609</answer>
<thinle
<thinle The image exhibits small counterclockwise Dutch angle, as evidenced the slight diagonal alignment of the cobblestone patlern and the parked cars, which are not perfectly parallel to the edges the frame. The vertical orientation shows small tilt-down, indicated by the horizon line being slightly above the center the image, suggesting a downward perspective. The ultra- wide-angle ffeld-of-view is apparent from the expansive view of the cobblestone area, the trees, captured with significant distortion the edges,
and the surrounding buildings, which particularly noticeable in the curvature of the cobblestone pattern and the elongated appearance of the trees and cars. (ahhik><<nswwer-4 0. 1460, -0.3487. 1.8083</answer>
The positioning of the clouds, which are not centered but rather skewed towards the bottom left, implies large counterclockwise Dutch angle, as the frame is rotated emphasize the diagonal orientation of the sky. The absence of any horizon line terrestrial features indicates a significant tilt-up. as the camera is pointed upwards to capture the vastness of the sky. The medium shot field-of-view evident from the balance between the open sky the presence of the clouds, providing sense of depth without including too much of the surrounding environment. dahhiko-answerr-0..700 0.7096, 0.708--lasswer>
<think
The image exhibits large clockwise Dutch angle, evidenced by diagonal orientation of the staircase and the railing, which are not aligned with the vertical or horizontal axes of the frame. The significant filt-down perspective is apparent from the low angle at which the floor and omate emblem are captured, emphasizing the depth and leading the viewer's eye downward. The medium shot field-of-view is indicated by the inclusion of both the detailed emblem on the floor and the upper portion of the staircase, providing a balanced view that captures the architectural elements without excessive cropping or expanse. <ithinle<answers 5280, 0.4158, 0.8800</answer>
<thinke
The image exhibits large counterclockwise Dutch angie, evidenced the diagonal alignment of the building's facade and the street, which tilts the scene to the left. The vertical orientation shows a significant tllt-up as the lower portion of the building and street are cropped out, emphasizing the upper levels and sky. The wide-angle field-of-view is apparent from the broad perspective that captures multiple stories the building and the surrounding environment, including the adjacent structures and street elements, all within a single frame. <tthink><answer--.... 0.3745. 1.402</answwrr>
<thinke
Figure A8: Visualization on spatial reasoning process for camera understanding. We highlight the reasoned spatially grounded visual cues regarding each camera parameter using different colors: roll, pitch, and FoV.
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Ground Truth
ParamNet
GeoCalib
Puffin (Ours)
Figure A9: Qualitative evaluations on the camera understanding methods with horizon line visualization.
We show the common cases (with architectures indoor) and challenging cases (with few geometric features significant tilted camera poses) the top and bottom, respectively.
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Figure A10: Our camera understanding on AIGC images OOenAl 2025 (left) and real-world photographs (right).
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Figure A11: Our camera-controllallc generation results with various camera configurations.
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serene rural landscape with clear blue sky, and scattered clouds, featuring ripenin golden-brown wheat in the foreground, set against siightly horizon under bright daylight.
A brick pathway leads to light texturex vine covered mall adomeed with pink flowers, set against backdrap small plants, fallen auturnn leaves, and naturally overgrown. serene outdoor garden.
(a) Texl-to-image generation with varying roll angles.
A curving road Iied with cherry blossoms full pink bloom creates tranquil canopy, filtaring sunlight and casting dappled shadows over lush green grass, inviting sense of springtmme brauty.
A serene custe warm orange and pink hues over vastlake, silhouetting Abtantt againsta deep blue sky, exuding tranqiil and picturesque ambiance.
(b) Text-to-image generation with varying pitch angles.
11
rugged mountain peak everlooks a vest, green valley below, with sharp clifss and dislant, layered mountains stretching into clear blue sky. amphasking the isc ation and dramatic beauty of the alpine landscape.
A serene lakeside scene with grassy foreground sloping gently calm, cloud dotted reflection in the water, framed distant trees and strutures, evōking peaceful, rural atmosphere.
(c) Text-to-inn Text-to-image generation with varying FoVs.
Figure A12: Text-to-image generation (single-view) with specific controls for each camera parameter.
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Figure A13; Image-to-image generation (cross-view) with varying yaw angles. The image with a red box
denotes the initial view, and the others are the generated views based on the yaw deviation from the previous view.
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Figure A14: World exploration results. The 3D reconstruction results are obtained by VGGT.
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3D Space with Limited Focus
Spatial Imagination
Imagination Description:
cozy Kitchen
Prompt: Givan an initial view the envirommett imagine
backsplash, and windoow
with sheer curtains
your extending
ilummnateed
warm
lighting
Referance image
Imagination Description:
Prompt: Given an inilial view the envirnnmntt imagine
fireplace, flat-acreen TV
wooden sland and
scane behind you.
dining area velble through
an open doorway.
Referenca Image
Imagination Description: A livin room with white couch, a small lafie. large
Prompt: Given inilial view the environment, imagine how the scene
outside.
extends behind you.
Image
Imagination Description: A group of people
Prompt: Given initial view the environment. imagine the scene extendinn to your right
galtheeed
large vincow some
standing
sealed the floor.
engaged conversation
or activlies.
Referonce
(a) Spatial imagination. The plausible imagination results are highlighted.
Input
Devigtion (Yarw/Plee
+0.26
+0.0
40.17
-0.11
40.05
Refarence
Input
Deviation (Yand/Plch))
+0.10
+0.0
40.11
-0.16
+0.19
+0.01
Reference
(b) Photographic guidance. The suggested deviations of the camera parameters (yaw/pitch) are highlighted. Figure A15: Examples of the spatial imagination and photographic guidance.
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Reviews:
[{"review_id": "KtoonWa3r4", "reviewer": "Reviewer_pAbj", "summary": "This paper proposes Puffin, a unified multimodal model for camera-centric understanding and generation. Specifically, Puffin formulates camera understanding as an AR text token generation task, and camera-centric generation as a learnable query + conditional diffusion generation task in a meta-query way. Both tasks are unified in an LLM with multiple additional components (a connector and diffusion model for generation, a visual encoder for understanding).  The authors further build a large-scale dataset, Puffin-4M, containing 4M vision-language-camera triplets, to facilitate this new paradigm. Extensive experiments on benchmarks demonstrate Puffin’s strong performance across both understanding and generation tasks.", "questions": "Please see weaknesses. \n\nBesides, I have one more question regarding whether understanding and generation can benefit each other. In this paper, the author(s) claim that unifying understanding and generation can help each other (to a significant degree). However, it seems that in other general unified image understanding and generation works, the mutual benefit is not quite clear or not very significant. Can you explain why?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Unified camera-centric understanding and generation framework: It's a novel and meaningful idea to unify camera understanding and controllable generation in one framework.\n2. Thinking with Camera: Interpreting camera parameters as text description bridges geometry and language, and further enables reasoning with the camera in understanding and generation tasks.\n3. The newly collected 4M high-quality camera-centric vision-language dataset should be very helpful to the community.\n4. The proposed model can outperform previous works in both understanding and generation tasks, covering multiple benchmarks. Also, the ablation comprehensively covers different components of the model.", "weaknesses": "1. Parameter comparison over previous works:  A clear analysis of model scale and computational cost is missing. Since Puffin integrates multiple large-scale model components (LLM, diffusion model, vision encoder), comparing the total parameter count and FLOPs with prior understanding and generation baselines (e.g., GeoCalib, PreciseCam, etc) is needed.\n2. Data vs. model contribution: It's not quite clear whether Puffin’s performance gains mainly come from the model architecture or the large new dataset (Puffin-4M). Ablation and fair comparison under the same training dataset would be useful.\n3. Multi-round conversation capability: While the paper discusses instruction tuning and cross-view reasoning, it is not clear whether the proposed model can generalize well on multi-turn interleaved understanding and generation. For example, first do generation, then understanding in the second round.", "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": "iKns4pSmfm", "reviewer": "Reviewer_j8cQ", "summary": "This work presents Puffin, a multimodal model that unified camera-centric generation and understanding. Puffin treats camera parameters such as pitch, yaw and FoC as discrete tokens, similar to language tokens, thereby enabling thinking with cameras. Puffin-4M is introduced which consists of vision-language-camera triplets, constructed by collecting panoramic images followed by perspective crops on different camera angles, and synthetic captions generated by a VLM. The camera understanding takes as input : text and camera discrete tokens, and image tokens from a geometry-aligned vision encoder, while the camera generation module additionally has a learnable connector module and camera maps as additional conditioning. After multi-stage training on Puffin-4M, experiments demonstrate Puffin’s superior performance over specialized models for camera-centric generation and understanding. Futher, with instruction tuning Puffin can be extended to cross-view tasks such as world exploration and spatial imagination.", "questions": "Please refer to weaknesses.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. The paper is easy to read and well-motivated; in that it is the first attempt to unify camera generation and understanding.\n2. Puffin outperforms existing baselines and methods across multiple benchmarks.\n3. The finding that representing and learning camera parameters as discrete tokens is an impactul finding; making them almost analogous with how text tokens are used in today's generative vision systems.\n4. The paper is technically dense; all design choices and the reasonings behind them are well documented.\n5. I believe the Puffin-4M dataset will be a great contribution to the community.", "weaknesses": "I have a few minor weakeness/comments : \n\n1. As shown in Table A1, the parameter-to-term mapping is not exhaustive; for example how is a small tilt-up with a clockwise\ndutch angle handled?\n2. Since, the camera parameter tokenizer is similar to the text tokenizer, is there any ablation that show the effect of different kinds of text encoder? For example, is there a performance delta in using encoder / encoder-decoder / decoder-only models?", "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": "JlgFELYDb5", "reviewer": "Reviewer_2Lvj", "summary": "The paper focuses on an interesting task -- unified camera-centric understanding and generation. There are two major contributions: they collect a 4M high-quality dataset with multiple labels for both mm understanding and generation, while they also train a unified VLM called Puffin for the target task. The proposed model achieved state-of-the-art performance on the camera-centric tasks and enables quite a few interesting applications. Overall, this paper positions “thinking with camera” as a step toward foundation‑level models that natively incorporate 3D geometry in both understanding and generation.", "questions": "- In A.3.3, the caption of the Puffin is generated by Qwen2.5VL will this explain why Qwen-image (reuse qwen2.5vl as encoder) performs better than GPT4o and Nano-banana? How to mitigate the affect of this issue? \n- Table A.3, bottom right cell, should be 0.2 rather than 0.05?\n- For thinking with camera, will RL, such as GRPO, help improve the reasoning capability? \n- Following the previous comment, it seems the thinking with camera training data is generated by Qwen2.5VL, how to make sure Qwen is able to provide some high-quality data since it is not trained on this task? Is it possible to measure their quality? \n- Are **camera tokens** robust to out‑of‑distribution intrinsics/extrinsics?\n- How does performance scale with data set size and quality?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper is well-structured and easy to follow\n- The framing of a single model and interface for both camera‑centric understanding and generation, with camera tokens as the key abstraction, is interesting (though it follows the popular way of designing unified models)\n- Puffin‑4M appears carefully constructed to supervise geometry/camera attributes across many scenes.\n- Superior performance compared with sotam odels, for both tasks.", "weaknesses": "- Missing specialized strong baselines: some comparisons to **strongest specialized 3D models** (e.g., recent camera calibration/pose methods) seem limited in the main text; more head‑to‑head numbers would strengthen claims.\n\n- L408-416: for generation comparisons, it is also useful to show standard generation evaluation metrics, such as FID -- perfect camera control makes no sense if the overall visual quality is poor. \n- Better to show the error bar for the compared models in the major experiments. \n- Details of thinking with camera is missing.", "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": "yS3L5gRpXJ", "reviewer": "Reviewer_D4GA", "summary": "The paper proposes Puffin, a unified multimodal framework that treats the camera as a first-class modality to both (1) understand camera geometry from images and (2) generate images under precise camera control. Key ideas include: “thinking with camera” (reasoning in photographic terms such as Dutch angle / tilt-up) to bridge numeric parameters and language; a geometry-aligned vision encoder; and a continuous camera latent via pixel-wise camera maps to condition a diffusion generator. A new Puffin-4M dataset (4M image–text–camera triplets) and evaluation sets (Puffin-Und, Puffin-Gen) are introduced. Puffin reports strong results on camera understanding across several datasets and large margins over LMM baselines and PreciseCam for camera-controllable generation; ablations suggest the “thinking” and camera-map latent help.", "questions": "1. Line 207, table A1 in the appendix should not be directly referred to in the main content.\n\n2. How sensitive are results to the exact photographic term thresholds (Table A1)? Any continuous-to-discrete ablation or learned bins?\n\n\n3. How does Puffin handle fisheye or smartphone ultrawide distortion at test time? Could the camera-map latent be extended with distortion fields?\n\n\n4. If you remove LLM-generated “thinking” (or replace with noisy/short versions), how quickly do understanding/generation scores degrade? Any attempts at self-consistency or rationale-free training?\n\n\n5. The abbreviation “FoV” was never introduced.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Precise, flexible camera control via discrete tokens plus a dense per-pixel camera map.\n\n\n2. Unified “think-with-camera” design that improves both understanding (pose/FoV) and controllable generation, and the concept itself is novel.\n\n\n3. Scales with large curated data and cleanly extends to new tasks and parameters.", "weaknesses": "1. Missing details on the construction of Puffin-Und and Puffin-Gen.\n\n2. Training at fixed 512 and use of central crop + resize for non-square inputs degrades understanding on datasets like LaMAR; this is acknowledged but might affect claims of generality.", "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)